Datasets:

davanstrien

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pytorchmodelmetadata

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pravesh/wav2vec2-large-xls-r-300m-hindi-colabrathee-intel

bad2c6336d229e13553bc0e88f2c18832b3ebcad

2022-05-31T07:04:30.000Z

automatic-speech-recognition

pravesh

pravesh/wav2vec2-large-xls-r-300m-hindi-colabrathee-intel

transformers

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-hindi-colabrathee-intel results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-hindi-colabrathee-intel This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

theojolliffe/bart-cnn-science-v3-e4

b9fc8cb7e6065dc2b41404a054146472b365feca

2022-05-31T09:41:01.000Z

text2text-generation

theojolliffe

theojolliffe/bart-cnn-science-v3-e4

transformers

--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-cnn-science-v3-e4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-cnn-science-v3-e4 This model is a fine-tuned version of [theojolliffe/bart-cnn-science](https://huggingface.co/theojolliffe/bart-cnn-science) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8265 - Rouge1: 53.0296 - Rouge2: 33.4957 - Rougel: 35.8876 - Rougelsum: 50.0786 - Gen Len: 141.5926 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 0.9965 | 52.4108 | 32.1506 | 35.0281 | 50.0368 | 142.0 | | 1.176 | 2.0 | 796 | 0.8646 | 52.7182 | 32.9681 | 35.1454 | 49.9527 | 141.8333 | | 0.7201 | 3.0 | 1194 | 0.8354 | 52.5417 | 32.6428 | 35.8703 | 49.8037 | 142.0 | | 0.5244 | 4.0 | 1592 | 0.8265 | 53.0296 | 33.4957 | 35.8876 | 50.0786 | 141.5926 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

huggingtweets/xvbones

bd45302e4e3132418393f36e2c3834f86656b74a

2022-05-31T08:53:32.000Z

text-generation

huggingtweets

huggingtweets/xvbones

transformers

--- language: en thumbnail: http://www.huggingtweets.com/xvbones/1653987207699/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1136186352268132354/PEn3hUdJ_400x400.png&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">tommy 🇬🇧</div> <div style="text-align: center; font-size: 14px;">@xvbones</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from tommy 🇬🇧. | Data | tommy 🇬🇧 | | --- | --- | | Tweets downloaded | 3161 | | Retweets | 205 | | Short tweets | 603 | | Tweets kept | 2353 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2gnhi9y4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @xvbones's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1hbqc87v) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1hbqc87v/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/xvbones') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

kamalkraj/bert-base-uncased-squad-v1.0-finetuned

4b54f4fac95f2ec34039e928b046f4c967701944

2022-05-31T10:25:59.000Z

question-answering

kamalkraj

kamalkraj/bert-base-uncased-squad-v1.0-finetuned

transformers

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-base-uncased-squad-v1.0-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-squad-v1.0-finetuned This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00012 - train_batch_size: 48 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.10.0+cu113 - Datasets 1.17.0 - Tokenizers 0.12.1

theojolliffe/bart-cnn-science-v3-e5

88af7d4c16ba03f3f2008f83fefe2087414434a9

2022-05-31T10:55:17.000Z

text2text-generation

theojolliffe

theojolliffe/bart-cnn-science-v3-e5

transformers

--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-cnn-science-v3-e5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-cnn-science-v3-e5 This model is a fine-tuned version of [theojolliffe/bart-cnn-science](https://huggingface.co/theojolliffe/bart-cnn-science) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8090 - Rouge1: 54.0053 - Rouge2: 35.5018 - Rougel: 37.3204 - Rougelsum: 51.5456 - Gen Len: 142.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 0.9935 | 51.9669 | 31.8139 | 34.4748 | 49.5311 | 141.7407 | | 1.1747 | 2.0 | 796 | 0.8565 | 51.7344 | 31.7341 | 34.3917 | 49.2488 | 141.7222 | | 0.7125 | 3.0 | 1194 | 0.8252 | 52.829 | 33.2332 | 35.8865 | 50.1883 | 141.5556 | | 0.4991 | 4.0 | 1592 | 0.8222 | 53.582 | 33.4906 | 35.7232 | 50.589 | 142.0 | | 0.4991 | 5.0 | 1990 | 0.8090 | 54.0053 | 35.5018 | 37.3204 | 51.5456 | 142.0 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

kamalkraj/bert-base-uncased-squad-v2.0-finetuned

df254ec6b3d72b17ea96833774484663bf8100af

2022-05-31T11:44:58.000Z

question-answering

kamalkraj

kamalkraj/bert-base-uncased-squad-v2.0-finetuned

transformers

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: bert-base-uncased-squad-v2.0-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-squad-v2.0-finetuned This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad_v2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00012 - train_batch_size: 48 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.10.0+cu113 - Datasets 1.17.0 - Tokenizers 0.12.1

huggingtweets/binance-dydx-magiceden

712fa0b65abbc6de05158e53d82db11e445024ea

2022-05-31T11:34:01.000Z

text-generation

huggingtweets

huggingtweets/binance-dydx-magiceden

transformers

--- language: en thumbnail: http://www.huggingtweets.com/binance-dydx-magiceden/1653996837144/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1529814669493682176/BqZU57Cf_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1490589455786573824/M5_HK15F_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1364590285255290882/hjnIm9bV_400x400.jpg&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Magic Eden 🪄 & Binance & dYdX</div> <div style="text-align: center; font-size: 14px;">@binance-dydx-magiceden</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Magic Eden 🪄 & Binance & dYdX. | Data | Magic Eden 🪄 | Binance | dYdX | | --- | --- | --- | --- | | Tweets downloaded | 3249 | 3250 | 1679 | | Retweets | 141 | 194 | 463 | | Short tweets | 908 | 290 | 40 | | Tweets kept | 2200 | 2766 | 1176 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/28typldl/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @binance-dydx-magiceden's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/196gmkng) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/196gmkng/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/binance-dydx-magiceden') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

huggingtweets/magiceden

8044a2914e378db23a563c4e25bc1a183272234f

2022-05-31T11:45:39.000Z

text-generation

huggingtweets

huggingtweets/magiceden

transformers

--- language: en thumbnail: http://www.huggingtweets.com/magiceden/1653997534626/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1529814669493682176/BqZU57Cf_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Magic Eden 🪄</div> <div style="text-align: center; font-size: 14px;">@magiceden</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Magic Eden 🪄. | Data | Magic Eden 🪄 | | --- | --- | | Tweets downloaded | 3249 | | Retweets | 141 | | Short tweets | 908 | | Tweets kept | 2200 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/9t2x97k9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @magiceden's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/32j65yat) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/32j65yat/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/magiceden') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

bmichele/poetry-generation-nextline-mbart-ws-sv-test

fc54fef6c4e43a10115046ae3ccc0accc9b292ac

2022-05-31T13:47:27.000Z

bmichele

bmichele/poetry-generation-nextline-mbart-ws-sv-test

Entry not found

mikehemberger/topex

bfb169a1ef98ed90cd31c1fb978697c002f2ec33

2022-05-31T16:51:57.000Z

image-classification

mikehemberger

mikehemberger/topex

transformers

Entry not found

tclong/wav2vec2-base-vios-v1

0da9b8eff7d0f7771edc5a2c28501ecdb2b6ce59

2022-06-02T11:33:05.000Z

automatic-speech-recognition

tclong

tclong/wav2vec2-base-vios-v1

transformers

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-vios-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-vios-v1 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6352 - Wer: 0.5161 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 5.7944 | 3.98 | 1000 | 1.7427 | 1.0387 | | 0.7833 | 7.97 | 2000 | 0.4026 | 0.4364 | | 0.4352 | 11.95 | 3000 | 0.3967 | 0.4042 | | 0.4988 | 15.94 | 4000 | 0.5446 | 0.4632 | | 0.7822 | 19.92 | 5000 | 0.6563 | 0.5491 | | 0.8496 | 23.9 | 6000 | 0.5828 | 0.5045 | | 0.8072 | 27.89 | 7000 | 0.6318 | 0.5109 | | 0.8336 | 31.87 | 8000 | 0.6352 | 0.5161 | | 0.8311 | 35.86 | 9000 | 0.6352 | 0.5161 | | 0.839 | 39.84 | 10000 | 0.6352 | 0.5161 | | 0.8297 | 43.82 | 11000 | 0.6352 | 0.5161 | | 0.8288 | 47.81 | 12000 | 0.6352 | 0.5161 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

voidful/wav2vec2-xlsr-53-espeak-librispeech-ft

00841b1da6316d7eca5dc56867b27a838a8b01cb

2022-06-04T12:02:47.000Z

automatic-speech-recognition

voidful

voidful/wav2vec2-xlsr-53-espeak-librispeech-ft

transformers

Entry not found

huggingtweets/gretathunberg

480fc5a85a9197c673d5763dfa616beea70836f4

2022-05-31T21:00:03.000Z

text-generation

huggingtweets

huggingtweets/gretathunberg

transformers

--- language: en thumbnail: http://www.huggingtweets.com/gretathunberg/1654030798001/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1459213153301053442/rL5hhpAI_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Greta Thunberg</div> <div style="text-align: center; font-size: 14px;">@gretathunberg</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Greta Thunberg. | Data | Greta Thunberg | | --- | --- | | Tweets downloaded | 3247 | | Retweets | 2424 | | Short tweets | 28 | | Tweets kept | 795 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ulsdxk8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gretathunberg's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/22acoony) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/22acoony/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gretathunberg') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

jppaolim/v39_Best20Epoch

1f9bf67e857808b702d3ad931de815df61504717

2022-05-31T21:42:21.000Z

text-generation

jppaolim

jppaolim/v39_Best20Epoch

transformers

# My Story model Arthur goes to the beach. Arthur is feeling very hot and bored. He decides to go to the beach. He goes to the beach. He spends the day swimming. Arthur cannot wait for the next day to go swimming. Arthur goes to the beach. Arthur wants to go to the beach. He gets a map. He looks at the map. He goes to the beach. He goes to the beach. Arthur goes to the beach. Arthur has been working hard all summer. He has been working hard every day. One day his boss asks him to come to work. Arthur is happy to see that his hard work is paying off. Arthur is so glad he took the chance to go to the beach. Arthur goes to the beach. Arthur is walking to the beach. He sees a small boy playing in the sand. The boy tells Arthur to leave. Arthur tells the boy he doesn't want to go to the beach. Arthur leaves the beach. Arthur goes to the beach. Arthur is a young boy who lived in a very small town. He wanted to feel like a big city kid. He drove to the coast and swam in the ocean. When he got home, his mom told him to pack up and come back. Arthur packed up and didn't go to the beach anymore. Arthur goes to the beach. Arthur is bored at home. He decides to go to the local beach. He goes down to the water. Arthur waves. He is glad he went for a walk down the beach. Arthur goes to the beach. Arthur wants to go to the beach. He has been looking forward to this for a week. He gets to the beach and everything feels perfect. He gets to the water and it is very nice. Arthur has the best day ever. Arthur goes to the beach. Arthur is going to the beach tomorrow. He is going to play in the ocean. He can't find his keys. He is starting to panic. Arthur finally finds his keys in his car. Arthur goes to the beach. Arthur is going to the beach tomorrow. He has been working hard all week. He is going to the beach with his friends. Arthur and his friends get in the car to go to the beach. Arthur swims all day and goes to sleep. Arthur goes to the beach. Arthur wants to go to the beach. He goes to the beach. He swims in the ocean. He has fun. Arthur has a good day. Arthur goes to the beach. Arthur is a young man. He likes to surf. He decides to go to the beach. He spends the whole day at the beach. He goes to the ocean and has fun. Arthur goes to the beach. Arthur is a young man. He wants to go to the beach. He gets on his car and drives to the beach. He spends the entire day at the beach. Arthur has the best day ever at the beach. Arthur goes to the beach. Arthur is a young man. He likes to surf and swim. He decides to go to the beach. Arthur swam all day long. He had a great day at the beach. Arthur goes to the beach. Arthur is going to the beach tomorrow. He has been working all day, but hasn't been swimming. He decides to go for a swim anyway and cool off. He spends the next few days playing in the ocean. Arthur has the time of his life. Arthur goes to the beach. Arthur is a young boy who lived in a very small town. He wanted to go to the beach but his dad said no. Arthur asked his dad if he could go alone. Arthur's dad told him that they couldn't afford to go together. Arthur was sad that his dad wouldn't go with him to the beach.

skr3178/xlm-roberta-base-finetuned-panx-de

3dd7f4d94a3698ec129db495509af67ddc3a768a

2022-05-31T22:09:30.000Z

token-classification

skr3178

skr3178/xlm-roberta-base-finetuned-panx-de

transformers

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8627004891366169 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1363 - F1: 0.8627 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2539 | 1.0 | 525 | 0.1697 | 0.8179 | | 0.1317 | 2.0 | 1050 | 0.1327 | 0.8516 | | 0.0819 | 3.0 | 1575 | 0.1363 | 0.8627 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

jppaolim/v40_NeoSmall

4bfb518bfe206f0aad0f40c12cfa1114e94a153a

2022-05-31T22:23:08.000Z

text-generation

jppaolim

jppaolim/v40_NeoSmall

transformers

# My Story model Arthur goes to the beach. Arthur is in the ocean. He is enjoying the water. He cannot wait for the sun to rise. He goes to the beach. It is very hot outside. Arthur goes to the beach. Arthur is going to the beach. He is going to the beach. He is going to go swimming. He feels a breeze on his shirt. He feels very relaxed. Arthur goes to the beach. Arthur is walking on the beach. He notices a sign for the beach club. He asks for a cab. He gets a cab to go to the beach. Arthur and his friends go to the beach together. Arthur goes to the beach. Arthur was excited to go to the beach. He drove his car to the beach. When he got there, he was amazed at the waves. The waves had a huge sandcastle. Arthur went to the beach and enjoyed the beach. Arthur goes to the beach. Arthur is playing in the sand with his friends. He is having a great time, and they are all laughing. They all seem to be enjoying themselves. Arthur decides he has to leave. Arthur is sad that he will not be able to go to the beach. Arthur goes to the beach. Arthur wants to go to the beach. He decides to go to the beach. He sees a sign for the beach. He goes to the beach. Arthur is happy to go to the beach. Arthur goes to the beach. Arthur is at the beach. He is playing with his friends. They go swimming. Arthur is caught in a water. Arthur is taken to the beach. Arthur goes to the beach. Arthur is in the ocean. He is bored. He decides to go to the beach. He is bored for a few hours. Arthur leaves the beach. Arthur goes to the beach. Arthur is out swimming. He is going to the beach. He goes to the beach. He goes to the beach. He goes to the beach. Arthur goes to the beach. Arthur was at the beach with his friends. They went swimming and laid out on the sand. They found a beach they liked. They decided to go to the beach and play. They were so happy that they decided to go back to the beach. Arthur goes to the beach. Arthur is at the beach with his family. They are going to go to the beach. Arthur is very excited. He is going to go to the beach. Arthur is happy that he went to the beach. Arthur goes to the beach. Arthur was at the beach with his friends. They were having a great time. They all went to the beach. They had a great time. Arthur is very happy. Arthur goes to the beach. Arthur is bored. He decides to go to the beach. He goes to the beach. He goes to the beach. He is happy that he went to the beach. Arthur goes to the beach. Arthur is bored. He decides to go to the beach. He is very bored. He decides to go to the beach. Arthur is happy that he went to the beach. Arthur goes to the beach. Arthur is on his way to the beach. He is going to the beach. He is going to the beach. He is going to the beach. Arthur is going to the beach.

skr3178/xlm-roberta-base-finetuned-panx-de-fr

20315cc1e9ca834750316a98caa11e8ee6fd50c5

2022-05-31T22:37:32.000Z

token-classification

skr3178

skr3178/xlm-roberta-base-finetuned-panx-de-fr

transformers

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1644 - F1: 0.8617 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2891 | 1.0 | 715 | 0.1780 | 0.8288 | | 0.1471 | 2.0 | 1430 | 0.1627 | 0.8509 | | 0.0947 | 3.0 | 2145 | 0.1644 | 0.8617 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

skr3178/xlm-roberta-base-finetuned-panx-fr

487e56bc1f9c5c516661cf1f09cf9c7001949cc4

2022-05-31T22:56:49.000Z

token-classification

skr3178

skr3178/xlm-roberta-base-finetuned-panx-fr

transformers

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.835464333781965 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2867 - F1: 0.8355 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5817 | 1.0 | 191 | 0.3395 | 0.7854 | | 0.2617 | 2.0 | 382 | 0.2856 | 0.8278 | | 0.1708 | 3.0 | 573 | 0.2867 | 0.8355 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

skr3178/xlm-roberta-base-finetuned-panx-it

6ca93c1d40bbb8c2a7df3d87cd6669f22603f55d

2022-05-31T23:14:06.000Z

token-classification

skr3178

skr3178/xlm-roberta-base-finetuned-panx-it

transformers

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8247845711940912 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2421 - F1: 0.8248 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.809 | 1.0 | 70 | 0.3380 | 0.7183 | | 0.2939 | 2.0 | 140 | 0.2582 | 0.7977 | | 0.1813 | 3.0 | 210 | 0.2421 | 0.8248 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

skr3178/xlm-roberta-base-finetuned-panx-en

c34a56a5c7d52d37e1e11a94bd17a1985e41e782

2022-05-31T23:31:12.000Z

token-classification

skr3178

skr3178/xlm-roberta-base-finetuned-panx-en

transformers

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.en metrics: - name: F1 type: f1 value: 0.692179700499168 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.3921 - F1: 0.6922 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1465 | 1.0 | 50 | 0.5838 | 0.4777 | | 0.5055 | 2.0 | 100 | 0.4477 | 0.6374 | | 0.3713 | 3.0 | 150 | 0.3921 | 0.6922 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

skr3178/xlm-roberta-base-finetuned-panx-all

36e4754f3b373ec510abd585ecb882d73ad203a0

2022-05-31T23:55:44.000Z

token-classification

skr3178

skr3178/xlm-roberta-base-finetuned-panx-all

transformers

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1752 - F1: 0.8557 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3 | 1.0 | 835 | 0.1862 | 0.8114 | | 0.1552 | 2.0 | 1670 | 0.1758 | 0.8426 | | 0.1002 | 3.0 | 2505 | 0.1752 | 0.8557 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

logo-data-science/t5-finetuned-eng

628a80defb1ab1fa18317a91cf912e9c3bf373db

2022-06-01T09:25:13.000Z

text2text-generation

logo-data-science

logo-data-science/t5-finetuned-eng

transformers

--- license: gpl ---

roshnir/mBert-finetuned-mlqa-dev-en-hi

d81c0d6cbe589b5d1010a54ba1e822152a7edc4c

2022-06-01T09:23:11.000Z

question-answering

roshnir

roshnir/mBert-finetuned-mlqa-dev-en-hi

transformers

Entry not found

pravesh/wav2vec2-large-xls-r-300m-Hindi-colab-v4

da059722c9738f4ae43ea65c493a9bff24b72d9a

2022-06-01T12:23:43.000Z

automatic-speech-recognition

pravesh

pravesh/wav2vec2-large-xls-r-300m-Hindi-colab-v4

transformers

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-Hindi-colab-v4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-Hindi-colab-v4 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

susghosh/roberta-large-squad

9877a5f5304d9addde3202ff65e8f74e2ba94600

2022-06-01T20:54:10.000Z

question-answering

susghosh

susghosh/roberta-large-squad

transformers

Entry not found

tmills/timex-thyme-colon-pubmedbert

5da811e37a51e1be102d7942b9d790dfad33122b

2022-06-01T17:04:40.000Z

tmills

tmills/timex-thyme-colon-pubmedbert

transformers

--- license: apache-2.0 ---

tmills/event-thyme-colon-pubmedbert

5b254eb8700ff818ea3a99075a15bb8a3243fe69

2022-06-01T17:08:50.000Z

tmills

tmills/event-thyme-colon-pubmedbert

transformers

--- license: apache-2.0 ---

jxm/u-PMLM-A

17fd0a7912328d72478ade337ae7c24ba3a408c9

2022-06-01T17:38:06.000Z

feature-extraction

jxm

jxm/u-PMLM-A

transformers

Entry not found

roshnir/mBert-finetuned-mlqa-dev-en

98687c5e4f42dec4078fc03132026595873f436e

2022-06-01T18:53:54.000Z

question-answering

roshnir

roshnir/mBert-finetuned-mlqa-dev-en

transformers

Entry not found

huggingtweets/disgustingact84-kickswish

1441e770706717413f2d1990cb1391af0a5cbb6b

2022-06-01T19:44:49.000Z

text-generation

huggingtweets

huggingtweets/disgustingact84-kickswish

transformers

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1258515252163022848/_O1bOXBQ_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1530279378332041220/1ysZA-S8_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Justin Moran & ToxicAct 🇺🇸 ⚽️</div> <div style="text-align: center; font-size: 14px;">@disgustingact84-kickswish</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Justin Moran & ToxicAct 🇺🇸 ⚽️. | Data | Justin Moran | ToxicAct 🇺🇸 ⚽️ | | --- | --- | --- | | Tweets downloaded | 3237 | 3247 | | Retweets | 286 | 260 | | Short tweets | 81 | 333 | | Tweets kept | 2870 | 2654 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3vwd4eeo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @disgustingact84-kickswish's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/24jluur0) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/24jluur0/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/disgustingact84-kickswish') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

huggingtweets/disgustingact84-kickswish-managertactical

26ab1ba6f9ce2703bcc2ea0b809c9a952c4516f3

2022-06-01T20:24:09.000Z

text-generation

huggingtweets

huggingtweets/disgustingact84-kickswish-managertactical

transformers

--- language: en thumbnail: http://www.huggingtweets.com/disgustingact84-kickswish-managertactical/1654115021712/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1530279378332041220/1ysZA-S8_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1258515252163022848/_O1bOXBQ_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1360389551336865797/6RERF_Gg_400x400.jpg&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">ToxicAct 🇺🇸 ⚽️ & Justin Moran & Tactical Manager</div> <div style="text-align: center; font-size: 14px;">@disgustingact84-kickswish-managertactical</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from ToxicAct 🇺🇸 ⚽️ & Justin Moran & Tactical Manager. | Data | ToxicAct 🇺🇸 ⚽️ | Justin Moran | Tactical Manager | | --- | --- | --- | --- | | Tweets downloaded | 3247 | 3237 | 3250 | | Retweets | 260 | 286 | 47 | | Short tweets | 333 | 81 | 302 | | Tweets kept | 2654 | 2870 | 2901 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3rtzdst3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @disgustingact84-kickswish-managertactical's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3lhxffhi) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3lhxffhi/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/disgustingact84-kickswish-managertactical') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

lmqg/t5-large-subjqa-books

be5be22dd3525016cac4d72f003a6dae90c58fc8

2022-06-02T14:06:26.000Z

text2text-generation

lmqg

lmqg/t5-large-subjqa-books

transformers

Entry not found

huggingtweets/mls_buzz-mlstransfers-transfersmls

3ed57533333a30e615444f9170027c6204110cbb

2022-06-01T20:57:13.000Z

text-generation

huggingtweets

huggingtweets/mls_buzz-mlstransfers-transfersmls

transformers

--- language: en thumbnail: http://www.huggingtweets.com/mls_buzz-mlstransfers-transfersmls/1654117028998/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1142613360854388738/C49XegQF_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/417716955076763648/_e97ys3b_400x400.jpeg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1229972304689614848/EqOwTdY8_400x400.jpg&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">MLS Buzz & MLS Transfers & Will Forbes</div> <div style="text-align: center; font-size: 14px;">@mls_buzz-mlstransfers-transfersmls</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from MLS Buzz & MLS Transfers & Will Forbes. | Data | MLS Buzz | MLS Transfers | Will Forbes | | --- | --- | --- | --- | | Tweets downloaded | 3250 | 3248 | 3247 | | Retweets | 32 | 811 | 1136 | | Short tweets | 167 | 475 | 359 | | Tweets kept | 3051 | 1962 | 1752 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/29rusxig/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mls_buzz-mlstransfers-transfersmls's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2qzhkike) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2qzhkike/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/mls_buzz-mlstransfers-transfersmls') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

lmqg/t5-large-subjqa-tripadvisor

1da63a185d382b5bdb32afb50b1a27098ff8a27b

2022-06-03T00:05:03.000Z

text2text-generation

lmqg

lmqg/t5-large-subjqa-tripadvisor

transformers

Entry not found

lmqg/t5-large-subjqa-grocery

d02f3b52d230f770f2186ad3d0c32e9fa64e6d2c

2022-06-02T18:12:47.000Z

text2text-generation

lmqg

lmqg/t5-large-subjqa-grocery

transformers

Entry not found

lmqg/t5-large-subjqa-movies

76dcc4a85e049f80d88c74d86ea82f6add3b287c

2022-06-02T20:08:07.000Z

text2text-generation

lmqg

lmqg/t5-large-subjqa-movies

transformers

Entry not found

lmqg/t5-large-subjqa-electronics

28752831479f8395accb27cb63806bc35ba25c6f

2022-06-02T16:10:34.000Z

text2text-generation

lmqg

lmqg/t5-large-subjqa-electronics

transformers

Entry not found

q2-jlbar/swin-tiny-patch4-window7-224-finetuned-eurosat

87867e84c73819fa3de2d8e872b8c52d8e0a42a2

2022-06-06T14:24:15.000Z

image-classification

q2-jlbar

q2-jlbar/swin-tiny-patch4-window7-224-finetuned-eurosat

transformers

--- license: apache-2.0 tags: - generated_from_trainer datasets: - image_folder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: image_folder type: image_folder args: default metrics: - name: Accuracy type: accuracy value: 0.9618518518518518 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.1199 - Accuracy: 0.9619 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3627 | 0.99 | 47 | 0.1988 | 0.9389 | | 0.2202 | 1.99 | 94 | 0.1280 | 0.9604 | | 0.1948 | 2.99 | 141 | 0.1199 | 0.9619 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0 - Datasets 2.2.2 - Tokenizers 0.12.1

lmqg/bart-large-subjqa-restaurants

5f0c532d3706e07a29827eb425c5f8007271644d

2022-06-05T01:54:16.000Z

text2text-generation

lmqg

lmqg/bart-large-subjqa-restaurants

transformers

Entry not found

lmqg/bart-large-subjqa-electronics

8aeb84dc1f30435c41f0582609da1d3801aa8fb3

2022-06-02T16:39:00.000Z

text2text-generation

lmqg

lmqg/bart-large-subjqa-electronics

transformers

Entry not found

lmqg/bart-base-subjqa-electronics

9648a6730585914b8480768e379037030b43988a

2022-06-02T16:19:36.000Z

text2text-generation

lmqg

lmqg/bart-base-subjqa-electronics

transformers

Entry not found

lmqg/t5-base-subjqa-restaurants

40bd8f30699609dd8c1e3f8868ad629dbb4ecb03

2022-06-02T21:13:18.000Z

text2text-generation

lmqg

lmqg/t5-base-subjqa-restaurants

transformers

Entry not found

lmqg/bart-large-subjqa-grocery

950812ff158c7678699f30cdc605cac27cff4873

2022-06-02T18:41:52.000Z

text2text-generation

lmqg

lmqg/bart-large-subjqa-grocery

transformers

Entry not found

lmqg/t5-small-subjqa-restaurants

a7ac86cb11875f81faf710d44ba73ecac2ec89e0

2022-06-02T20:47:41.000Z

text2text-generation

lmqg

lmqg/t5-small-subjqa-restaurants

transformers

Entry not found

lmqg/bart-base-subjqa-grocery

e46f0856298216b4a860ef031eafb5a5e5c242fe

2022-06-02T18:22:27.000Z

text2text-generation

lmqg

lmqg/bart-base-subjqa-grocery

transformers

Entry not found

lmqg/bart-base-subjqa-restaurants

68369cdab20879dcbaf31c552d277f5c727c3867

2022-06-02T22:16:28.000Z

text2text-generation

lmqg

lmqg/bart-base-subjqa-restaurants

transformers

Entry not found

lmqg/bart-base-subjqa-tripadvisor

b93f124e61a059266c8551be5ebbacae3cc7b2f8

2022-06-03T00:14:50.000Z

text2text-generation

lmqg

lmqg/bart-base-subjqa-tripadvisor

transformers

Entry not found

lmqg/t5-base-subjqa-tripadvisor

bcba1f71a0c67150a610b7987dadd8d9ea0d8931

2022-06-02T23:10:03.000Z

text2text-generation

lmqg

lmqg/t5-base-subjqa-tripadvisor

transformers

Entry not found

lmqg/t5-small-subjqa-books

b5e956cb7f8ca1a4ab692ec6a0359db79238b217

2022-06-02T12:50:00.000Z

text2text-generation

lmqg

lmqg/t5-small-subjqa-books

transformers

Entry not found

lmqg/bart-large-subjqa-tripadvisor

fa31c27640d49af60465d9280bcbdd6f395b0f0c

2022-06-03T00:33:54.000Z

text2text-generation

lmqg

lmqg/bart-large-subjqa-tripadvisor

transformers

Entry not found

lmqg/t5-small-subjqa-grocery

852b621cdc511fc0e9256e06f465e93c0411c760

2022-06-02T16:49:05.000Z

text2text-generation

lmqg

lmqg/t5-small-subjqa-grocery

transformers

Entry not found

lmqg/bart-base-subjqa-books

b6151618d12950764d3d90cafda3305e97a2a517

2022-06-02T14:17:43.000Z

text2text-generation

lmqg

lmqg/bart-base-subjqa-books

transformers

Entry not found

lmqg/t5-base-subjqa-grocery

07f0e720d9f5a7e5a0100d8e5ddf288281a0c6b8

2022-06-02T17:14:16.000Z

text2text-generation

lmqg

lmqg/t5-base-subjqa-grocery

transformers

Entry not found

lmqg/t5-base-subjqa-movies

f5941f6f7f3ec1d32f19d27f5224f8b1dd1b64f5

2022-06-02T19:13:30.000Z

text2text-generation

lmqg

lmqg/t5-base-subjqa-movies

transformers

Entry not found

lmqg/t5-small-subjqa-electronics

20c7817a80a9080d99478e8be70ffce442c87634

2022-06-02T14:53:45.000Z

text2text-generation

lmqg

lmqg/t5-small-subjqa-electronics

transformers

Entry not found

lmqg/t5-small-subjqa-tripadvisor

5d04b8b5376b2742063fee0e5527224bad456971

2022-06-02T22:45:32.000Z

text2text-generation

lmqg

lmqg/t5-small-subjqa-tripadvisor

transformers

Entry not found

lmqg/bart-large-subjqa-movies

72df74c24f2e08694dacf3d2e2e6abff7add6c44

2022-06-02T20:36:05.000Z

text2text-generation

lmqg

lmqg/bart-large-subjqa-movies

transformers

Entry not found

lmqg/bart-base-subjqa-movies

1e1363380cc00d481ddbf09b170624f26bb2fd0c

2022-06-02T20:17:56.000Z

text2text-generation

lmqg

lmqg/bart-base-subjqa-movies

transformers

Entry not found

x574chen/wav2vec2-common_voice-tr-demo

62f61685312cf5e71be40276255b245fad4e7508

2022-06-05T17:14:10.000Z

automatic-speech-recognition

x574chen

x574chen/wav2vec2-common_voice-tr-demo

transformers

--- language: - tr license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-common_voice-tr-demo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-common_voice-tr-demo This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.3815 - Wer: 0.3493 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.92 | 100 | 3.5559 | 1.0 | | No log | 1.83 | 200 | 3.0161 | 0.9999 | | No log | 2.75 | 300 | 0.8587 | 0.7443 | | No log | 3.67 | 400 | 0.5855 | 0.6121 | | 3.1095 | 4.59 | 500 | 0.4841 | 0.5204 | | 3.1095 | 5.5 | 600 | 0.4533 | 0.4923 | | 3.1095 | 6.42 | 700 | 0.4157 | 0.4342 | | 3.1095 | 7.34 | 800 | 0.4304 | 0.4334 | | 3.1095 | 8.26 | 900 | 0.4097 | 0.4068 | | 0.2249 | 9.17 | 1000 | 0.4049 | 0.3881 | | 0.2249 | 10.09 | 1100 | 0.3993 | 0.3809 | | 0.2249 | 11.01 | 1200 | 0.3855 | 0.3782 | | 0.2249 | 11.93 | 1300 | 0.3923 | 0.3713 | | 0.2249 | 12.84 | 1400 | 0.3833 | 0.3591 | | 0.1029 | 13.76 | 1500 | 0.3811 | 0.3570 | | 0.1029 | 14.68 | 1600 | 0.3834 | 0.3499 | ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.12.0a0+2c916ef - Datasets 2.2.2 - Tokenizers 0.10.3

huggingtweets/contextmemlab-jeremyrmanning

22c78ab17d30fdf47bf53fe197c1d8b895cc9b02

2022-06-02T06:59:24.000Z

text-generation

huggingtweets

huggingtweets/contextmemlab-jeremyrmanning

transformers

--- language: en thumbnail: http://www.huggingtweets.com/contextmemlab-jeremyrmanning/1654153159177/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1268155013882396672/Ev_5MJ-E_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/733324858621341698/iW5s1aAc_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Jeremy Manning & Context Lab</div> <div style="text-align: center; font-size: 14px;">@contextmemlab-jeremyrmanning</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Jeremy Manning & Context Lab. | Data | Jeremy Manning | Context Lab | | --- | --- | --- | | Tweets downloaded | 1635 | 206 | | Retweets | 1093 | 44 | | Short tweets | 88 | 1 | | Tweets kept | 454 | 161 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1383c0di/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @contextmemlab-jeremyrmanning's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1nunflkl) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1nunflkl/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/contextmemlab-jeremyrmanning') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

huggingtweets/paxt0n4

9fb6b0496a95b9e51d6843d948feca32cd23c738

2022-06-02T07:30:57.000Z

text-generation

huggingtweets

huggingtweets/paxt0n4

transformers

--- language: en thumbnail: http://www.huggingtweets.com/paxt0n4/1654155052782/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1359906890340306950/s5cXHS11_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Paxton Fitzpatrick</div> <div style="text-align: center; font-size: 14px;">@paxt0n4</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Paxton Fitzpatrick. | Data | Paxton Fitzpatrick | | --- | --- | | Tweets downloaded | 2551 | | Retweets | 1177 | | Short tweets | 326 | | Tweets kept | 1048 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1x9k9uk2/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @paxt0n4's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/34fd5zca) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/34fd5zca/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/paxt0n4') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

nglaura/skimformer

15a959f372085ee51e4afe20ce9812dafe355b21

2022-06-02T15:37:12.000Z

fill-mask

nglaura

nglaura/skimformer

transformers

--- license: apache-2.0 --- # Skimformer A collaboration between [reciTAL](https://recital.ai/en/) & [MLIA](https://mlia.lip6.fr/) (ISIR, Sorbonne Université) ## Model description Skimformer is a two-stage Transformer that replaces self-attention with Skim-Attention, a self-attention module that computes attention solely based on the 2D positions of tokens in the page. The model adopts a two-step approach: first, the skim-attention scores are computed once and only once using layout information alone; then, these attentions are used in every layer of a text-based Transformer encoder. For more details, please refer to our paper: [Skim-Attention: Learning to Focus via Document Layout](https://arxiv.org/abs/2109.01078) Laura Nguyen, Thomas Scialom, Jacopo Staiano, Benjamin Piwowarski, [EMNLP 2021](https://2021.emnlp.org/papers) ## Citation ``` latex @article{nguyen2021skimattention, title={Skim-Attention: Learning to Focus via Document Layout}, author={Laura Nguyen and Thomas Scialom and Jacopo Staiano and Benjamin Piwowarski}, journal={arXiv preprint arXiv:2109.01078} year={2021}, } ```

huggingtweets/eurovision

188ae8eeadf2f53d34d821abc4b60c5f9d880f21

2022-06-02T12:18:15.000Z

text-generation

huggingtweets

huggingtweets/eurovision

transformers

--- language: en thumbnail: http://www.huggingtweets.com/eurovision/1654172290217/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1531770686309646338/i1LUNPuy_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Eurovision Song Contest</div> <div style="text-align: center; font-size: 14px;">@eurovision</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Eurovision Song Contest. | Data | Eurovision Song Contest | | --- | --- | | Tweets downloaded | 3249 | | Retweets | 483 | | Short tweets | 146 | | Tweets kept | 2620 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/28rylgus/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @eurovision's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/wfx4mn2i) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/wfx4mn2i/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/eurovision') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

huggingtweets/esfinn

bf20e894fdd35d19197d36e16c9196921336cda2

2022-06-02T12:35:17.000Z

text-generation

huggingtweets

huggingtweets/esfinn

transformers

--- language: en thumbnail: http://www.huggingtweets.com/esfinn/1654173312571/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/773905129129046016/EZcRPMpd_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Emily Finn</div> <div style="text-align: center; font-size: 14px;">@esfinn</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Emily Finn. | Data | Emily Finn | | --- | --- | | Tweets downloaded | 767 | | Retweets | 209 | | Short tweets | 72 | | Tweets kept | 486 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/22n1p2vw/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @esfinn's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/caz2a2vq) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/caz2a2vq/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/esfinn') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

huggingtweets/gaytimes-grindr

af7c10e1efdfccba41b7e857947b478ffda2a045

2022-06-02T12:50:16.000Z

text-generation

huggingtweets

huggingtweets/gaytimes-grindr

transformers

--- language: en thumbnail: http://www.huggingtweets.com/gaytimes-grindr/1654174210818/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1531896348416483329/bPsiy7hP_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1456594968383041538/Ab0hl5Xl_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Grindr & GAY TIMES</div> <div style="text-align: center; font-size: 14px;">@gaytimes-grindr</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Grindr & GAY TIMES. | Data | Grindr | GAY TIMES | | --- | --- | --- | | Tweets downloaded | 3237 | 3250 | | Retweets | 149 | 239 | | Short tweets | 749 | 32 | | Tweets kept | 2339 | 2979 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/10mzbkyr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gaytimes-grindr's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/k5enwplb) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/k5enwplb/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gaytimes-grindr') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

huggingtweets/eurunuela

6423356e88cddf05cc2b5c74f3b08ab95006ffeb

2022-06-02T12:50:57.000Z

text-generation

huggingtweets

huggingtweets/eurunuela

transformers

--- language: en thumbnail: http://www.huggingtweets.com/eurunuela/1654174252782/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1476203864063893505/j7Ep0Muv_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Eneko Uruñuela</div> <div style="text-align: center; font-size: 14px;">@eurunuela</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Eneko Uruñuela. | Data | Eneko Uruñuela | | --- | --- | | Tweets downloaded | 1267 | | Retweets | 241 | | Short tweets | 42 | | Tweets kept | 984 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3fhgg7tg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @eurunuela's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ndd7uaz) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ndd7uaz/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/eurunuela') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

huggingtweets/claregrall

db9593a8c6a7d951570ae5b5016d393e657a4ba9

2022-06-02T13:17:25.000Z

text-generation

huggingtweets

huggingtweets/claregrall

transformers

--- language: en thumbnail: http://www.huggingtweets.com/claregrall/1654175841134/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1197255800114339842/9ptyNMcO_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Clare Grall</div> <div style="text-align: center; font-size: 14px;">@claregrall</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Clare Grall. | Data | Clare Grall | | --- | --- | | Tweets downloaded | 873 | | Retweets | 176 | | Short tweets | 51 | | Tweets kept | 646 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3fu0nxex/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @claregrall's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2yox9655) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2yox9655/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/claregrall') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

huggingtweets/willsavino

cc33ca7fce117c289e77166b7114af72efe2d2d7

2022-06-02T13:06:29.000Z

text-generation

huggingtweets

huggingtweets/willsavino

transformers

--- language: en thumbnail: http://www.huggingtweets.com/willsavino/1654175184979/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1078115982768525317/wk6NTSE0_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Will Savino</div> <div style="text-align: center; font-size: 14px;">@willsavino</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Will Savino. | Data | Will Savino | | --- | --- | | Tweets downloaded | 3229 | | Retweets | 355 | | Short tweets | 244 | | Tweets kept | 2630 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2nhwww0u/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @willsavino's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3k5ueoap) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3k5ueoap/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/willsavino') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

wvangils/DistilGPT2-Beatles-Lyrics-finetuned-newlyrics

ba697192b3f5436eeab752b1633c4dd5de2da2fa

2022-06-17T11:20:50.000Z

text-generation

wvangils

wvangils/DistilGPT2-Beatles-Lyrics-finetuned-newlyrics

transformers

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: DistilGPT2-Beatles-Lyrics-finetuned-newlyrics results: [] widget: - text: "Last night in Kiev the" example_title: "Kiev" - text: "It hasn't rained in weeks" example_title: "Rain" --- # DistilGPT2-Beatles-Lyrics-finetuned-newlyrics This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the [Cmotions - Beatles lyrics](https://huggingface.co/datasets/cmotions/Beatles_lyrics) dataset. It will complete an input prompt with Beatles-like text. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.786 | 1.0 | 18 | 2.0410 | | 2.5587 | 2.0 | 36 | 1.9280 | | 2.3651 | 3.0 | 54 | 1.8829 | | 2.2759 | 4.0 | 72 | 1.8473 | | 2.1241 | 5.0 | 90 | 1.8237 | | 2.1018 | 6.0 | 108 | 1.8535 | | 1.8537 | 7.0 | 126 | 1.8497 | | 1.7859 | 8.0 | 144 | 1.8618 | | 1.69 | 9.0 | 162 | 1.8657 | | 1.6481 | 10.0 | 180 | 1.8711 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

huggingtweets/quora-reddit

782bcfb2ef51280c4030a05b8eeb933f4672bd6f

2022-06-03T12:09:44.000Z

text-generation

huggingtweets

huggingtweets/quora-reddit

transformers

--- language: en thumbnail: http://www.huggingtweets.com/quora-reddit/1654258179125/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1532031893318737920/N4nwSAZv_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1333471260483801089/OtTAJXEZ_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Quora & Reddit</div> <div style="text-align: center; font-size: 14px;">@quora-reddit</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Quora & Reddit. | Data | Quora | Reddit | | --- | --- | --- | | Tweets downloaded | 3244 | 3248 | | Retweets | 181 | 331 | | Short tweets | 22 | 392 | | Tweets kept | 3041 | 2525 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/12sw605d/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @quora-reddit's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2g51clcs) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2g51clcs/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/quora-reddit') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

tclong/wav2vec2-base-vios-v2

fc29afd0af8c16648e852c591a9e5c0d7c0c059c

2022-06-06T17:45:04.000Z

automatic-speech-recognition

tclong

tclong/wav2vec2-base-vios-v2

transformers

--- license: apache-2.0 tags: - generated_from_trainer datasets: - vivos_dataset model-index: - name: wav2vec2-base-vios-v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-vios-v2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the vivos_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.6056 - Wer: 0.2442 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 7.8344 | 0.69 | 500 | 3.5012 | 1.0 | | 3.4505 | 1.37 | 1000 | 3.4081 | 1.0 | | 2.1426 | 2.06 | 1500 | 0.8761 | 0.6241 | | 0.8801 | 2.74 | 2000 | 0.5476 | 0.4241 | | 0.6453 | 3.43 | 2500 | 0.4384 | 0.3495 | | 0.5449 | 4.12 | 3000 | 0.4055 | 0.3160 | | 0.4862 | 4.8 | 3500 | 0.3815 | 0.3002 | | 0.4435 | 5.49 | 4000 | 0.3525 | 0.2776 | | 0.4205 | 6.17 | 4500 | 0.3660 | 0.2725 | | 0.3974 | 6.86 | 5000 | 0.3386 | 0.2565 | | 0.3758 | 7.54 | 5500 | 0.3492 | 0.2607 | | 0.3595 | 8.23 | 6000 | 0.3391 | 0.2441 | | 0.3438 | 8.92 | 6500 | 0.3255 | 0.2354 | | 0.3308 | 9.6 | 7000 | 0.3379 | 0.2422 | | 0.3265 | 10.29 | 7500 | 0.3375 | 0.2349 | | 0.311 | 10.97 | 8000 | 0.3356 | 0.2306 | | 0.3071 | 11.66 | 8500 | 0.3286 | 0.2249 | | 0.2941 | 12.35 | 9000 | 0.3176 | 0.2211 | | 0.296 | 13.03 | 9500 | 0.3268 | 0.2257 | | 0.2852 | 13.72 | 10000 | 0.3265 | 0.2196 | | 0.3102 | 14.4 | 10500 | 0.3390 | 0.2209 | | 0.2974 | 15.09 | 11000 | 0.3493 | 0.2199 | | 0.3433 | 15.78 | 11500 | 0.3687 | 0.2199 | | 0.3526 | 16.46 | 12000 | 0.3698 | 0.2170 | | 0.36 | 17.15 | 12500 | 0.4110 | 0.2227 | | 0.4322 | 17.83 | 13000 | 0.4830 | 0.2290 | | 0.4973 | 18.52 | 13500 | 0.5280 | 0.2356 | | 0.5701 | 19.2 | 14000 | 0.5990 | 0.2370 | | 0.6014 | 19.89 | 14500 | 0.6056 | 0.2442 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

finiteautomata/pepe

08f0926173c7a344390e8c3363f8d9b59b99d8d0

2022-06-02T13:53:07.000Z

feature-extraction

finiteautomata

finiteautomata/pepe

transformers

Entry not found

huggingtweets/vborghesani

2a9f827021a64002f177bd4f267b66d4f2a77035

2022-06-02T14:00:46.000Z

text-generation

huggingtweets

huggingtweets/vborghesani

transformers

--- language: en thumbnail: http://www.huggingtweets.com/vborghesani/1654178225151/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1279408626877304833/28JtkdiE_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Valentina Borghesani</div> <div style="text-align: center; font-size: 14px;">@vborghesani</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Valentina Borghesani. | Data | Valentina Borghesani | | --- | --- | | Tweets downloaded | 1024 | | Retweets | 140 | | Short tweets | 23 | | Tweets kept | 861 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21epnhoj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @vborghesani's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1vf22msq) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1vf22msq/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/vborghesani') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

huggingtweets/ppinheirochagas

b7c22f596eca05a22e9ce0c4a282b3637325caf1

2022-06-02T17:24:17.000Z

text-generation

huggingtweets

huggingtweets/ppinheirochagas

transformers

--- language: en thumbnail: http://www.huggingtweets.com/ppinheirochagas/1654190652962/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1510853995690033153/-mRCiWB0_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Pedro Pinheiro-Chagas</div> <div style="text-align: center; font-size: 14px;">@ppinheirochagas</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Pedro Pinheiro-Chagas. | Data | Pedro Pinheiro-Chagas | | --- | --- | | Tweets downloaded | 1001 | | Retweets | 658 | | Short tweets | 95 | | Tweets kept | 248 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1f73x4s5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ppinheirochagas's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/10v1i51v) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/10v1i51v/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/ppinheirochagas') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

huggingtweets/rauschermri

7feda5e6c7717e00411660d8fa24f13d726e404c

2022-06-02T18:12:11.000Z

text-generation

huggingtweets

huggingtweets/rauschermri

transformers

--- language: en thumbnail: http://www.huggingtweets.com/rauschermri/1654193526819/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1504854177993744386/k8Tb-5zg_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Alexander Rauscher</div> <div style="text-align: center; font-size: 14px;">@rauschermri</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Alexander Rauscher. | Data | Alexander Rauscher | | --- | --- | | Tweets downloaded | 3245 | | Retweets | 651 | | Short tweets | 253 | | Tweets kept | 2341 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/clzasreo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @rauschermri's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/e0w0wjmj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/e0w0wjmj/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/rauschermri') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

victorlee071200/distilbert-base-uncased-finetuned-squad

12bea89b51aee18d0315235fc9d5f2ab694ce6bd

2022-06-02T20:55:20.000Z

question-answering

victorlee071200

victorlee071200/distilbert-base-uncased-finetuned-squad

transformers

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1458 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2145 | 1.0 | 5533 | 1.1624 | | 0.9531 | 2.0 | 11066 | 1.1257 | | 0.7566 | 3.0 | 16599 | 1.1458 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

roshnir/mBert-finetuned-mlqa-dev-ar-hi

baa8582a3f202c5b8387fad95b0f32b74cd016a7

2022-06-02T20:27:21.000Z

question-answering

roshnir

roshnir/mBert-finetuned-mlqa-dev-ar-hi

transformers

Entry not found

huggingtweets/mrikasper

055f8a83161adba5971719e61b6c7aa066ba3fab

2022-06-02T21:40:46.000Z

text-generation

huggingtweets

huggingtweets/mrikasper

transformers

--- language: en thumbnail: http://www.huggingtweets.com/mrikasper/1654206041092/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/914206875419332608/26FrQMV2_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Lars Kasper</div> <div style="text-align: center; font-size: 14px;">@mrikasper</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Lars Kasper. | Data | Lars Kasper | | --- | --- | | Tweets downloaded | 475 | | Retweets | 113 | | Short tweets | 10 | | Tweets kept | 352 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3lbnyiin/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mrikasper's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1y754vcz) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1y754vcz/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/mrikasper') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

huggingtweets/the_dealersh1p

69dea5bd8e15b623ded08201a67ccf95b97ff718

2022-06-03T06:04:47.000Z

text-generation

huggingtweets

huggingtweets/the_dealersh1p

transformers

--- language: en thumbnail: http://www.huggingtweets.com/the_dealersh1p/1654236282143/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1211158441504456704/dCNSnY4k_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">『 』『dan』『 』</div> <div style="text-align: center; font-size: 14px;">@the_dealersh1p</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 『 』『dan』『 』. | Data | 『 』『dan』『 』 | | --- | --- | | Tweets downloaded | 2645 | | Retweets | 1314 | | Short tweets | 234 | | Tweets kept | 1097 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ps3bmmz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @the_dealersh1p's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/lh2yijs1) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/lh2yijs1/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/the_dealersh1p') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

huggingtweets/marazack26

3bde19e77d4eb2bb6794c76b00d2b1fb1239e61d

2022-06-02T22:56:49.000Z

text-generation

huggingtweets

huggingtweets/marazack26

transformers

--- language: en thumbnail: http://www.huggingtweets.com/marazack26/1654210546142/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1239803946643927041/AHuDYsfL_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Mohammed Abd Al-Razack / محمد عبد الرزاق</div> <div style="text-align: center; font-size: 14px;">@marazack26</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Mohammed Abd Al-Razack / محمد عبد الرزاق. | Data | Mohammed Abd Al-Razack / محمد عبد الرزاق | | --- | --- | | Tweets downloaded | 3060 | | Retweets | 1619 | | Short tweets | 167 | | Tweets kept | 1274 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/264mzr04/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @marazack26's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3p7448r6) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3p7448r6/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/marazack26') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

huggingtweets/joanacspinto

d2a93f6890c1ee8fc629654399d8cfa68c81098b

2022-06-02T23:02:58.000Z

text-generation

huggingtweets

huggingtweets/joanacspinto

transformers

--- language: en thumbnail: http://www.huggingtweets.com/joanacspinto/1654210973627/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1274320386881183747/NJTJm38e_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Dr Joana Pinto</div> <div style="text-align: center; font-size: 14px;">@joanacspinto</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Dr Joana Pinto. | Data | Dr Joana Pinto | | --- | --- | | Tweets downloaded | 177 | | Retweets | 50 | | Short tweets | 7 | | Tweets kept | 120 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/11osdq6n/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @joanacspinto's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/18w57bkp) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/18w57bkp/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/joanacspinto') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

meetyildiz/TurQA-xlm-roberta-base-finetuned-toqad

f7a716dd4d6fca32b8be1f40ee2a6392ee0f2b6d

2022-06-02T23:17:15.000Z

feature-extraction

meetyildiz

meetyildiz/TurQA-xlm-roberta-base-finetuned-toqad

transformers

Entry not found

victorlee071200/distilroberta-base-finetuned-squad

2bb429422b5af8cafcbbcfddd13e9cde793b27a8

2022-06-09T04:57:17.000Z

question-answering

victorlee071200

victorlee071200/distilroberta-base-finetuned-squad

transformers

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilroberta-base-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilroberta-base-finetuned-squad This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.0014 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0927 | 1.0 | 5536 | 1.0290 | | 0.87 | 2.0 | 11072 | 0.9683 | | 0.7335 | 3.0 | 16608 | 1.0014 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

meetyildiz/TurQA-convbert-base-turkish-cased-finetuned-toqad-aug

fa0eb23c0579d7c62abddef1ad4d61e23808a560

2022-06-02T23:32:46.000Z

feature-extraction

meetyildiz

meetyildiz/TurQA-convbert-base-turkish-cased-finetuned-toqad-aug

transformers

Entry not found

meetyildiz/TurQA-bert-base-turkish-cased-finetuned-toqad-aug

fc3a4258083cc90b852099fdda8118b088a3a991

2022-06-02T23:39:02.000Z

feature-extraction

meetyildiz

meetyildiz/TurQA-bert-base-turkish-cased-finetuned-toqad-aug

transformers

Entry not found

meetyildiz/TurQA-electra-base-turkish-cased-discriminator-finetuned-toqad-aug

150ba04e04cdb5d4dd7691108149a3300aeec183

2022-06-02T23:45:22.000Z

feature-extraction

meetyildiz

meetyildiz/TurQA-electra-base-turkish-cased-discriminator-finetuned-toqad-aug

transformers

Entry not found

meetyildiz/TurQA-xlm-roberta-base-finetuned-toqad-aug

70d357ccc95c8973f030436b26626131440199fd

2022-06-02T23:52:05.000Z

feature-extraction

meetyildiz

meetyildiz/TurQA-xlm-roberta-base-finetuned-toqad-aug

transformers

Entry not found

meetyildiz/TurQA-bert-base-turkish-128k-cased-finetuned-toqad-aug

f8a663906bb2f6214702a37e4b8fd9f351c201e8

2022-06-03T00:07:17.000Z

feature-extraction

meetyildiz

meetyildiz/TurQA-bert-base-turkish-128k-cased-finetuned-toqad-aug

transformers

Entry not found

johnny9604/pbl_electra

46c53265b2bedb70fd0a6bc4582586b853ad9cfa

2022-06-03T07:56:44.000Z

token-classification

johnny9604

johnny9604/pbl_electra

transformers

Entry not found

sriiikar/wav2vec2-hindi-bhoj-3

c8df3bfe3bb687852e6c444f5cb35a0ab36a8df5

2022-06-03T07:11:46.000Z

automatic-speech-recognition

sriiikar

sriiikar/wav2vec2-hindi-bhoj-3

transformers

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-hindi-bhoj-3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-hindi-bhoj-3 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.7033 - Wer: 1.1477 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 8.6136 | 6.45 | 400 | 3.6017 | 1.0 | | 2.6692 | 12.9 | 800 | 4.5408 | 1.0872 | | 0.5639 | 19.35 | 1200 | 5.2302 | 1.2282 | | 0.2296 | 25.8 | 1600 | 5.3323 | 1.0872 | | 0.1496 | 32.26 | 2000 | 5.7219 | 1.1342 | | 0.1098 | 38.7 | 2400 | 5.7033 | 1.1477 | ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.2.3.dev0 - Tokenizers 0.12.1

wogkr810/mnm

d82275332a586d98f9b729f266c8871f757f36d1

2022-06-05T05:03:24.000Z

question-answering

wogkr810

wogkr810/mnm

transformers

# Model --- ## [KLUE MRC 대회](https://github.com/boostcampaitech3/level2-mrc-level2-nlp-09)에서 사용한 Reader SOTA모델을 사용하여 파인튜닝을 진행했습니다. - 데이터셋 : 태깅 이후 구축한 데이터셋에서 전처리 및 Augmentation 적용 - [Huggingface : MRC Reader SOTA 모델](https://huggingface.co/Nonegom/roberta_finetune_twice) - [Github Issue : MRC Redaer SOTA 모델 설명](https://github.com/boostcampaitech3/level2-mrc-level2-nlp-09/issues/38)

huggingtweets/mundodeportivo

c2f817a2872f38e094190f0f14b0413e01662823

2022-06-03T09:09:48.000Z

text-generation

huggingtweets

huggingtweets/mundodeportivo

transformers

--- language: en thumbnail: http://www.huggingtweets.com/mundodeportivo/1654247301367/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1277369340275437570/R-AXlYNT_400x400.png&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Mundo Deportivo</div> <div style="text-align: center; font-size: 14px;">@mundodeportivo</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Mundo Deportivo. | Data | Mundo Deportivo | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 195 | | Short tweets | 26 | | Tweets kept | 3029 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/17m7lnrt/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mundodeportivo's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2mndpk3u) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2mndpk3u/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/mundodeportivo') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

kimcando/ko-paraKQC-demo2

1fd0fdeb8c0094b40a312488cdaa5d53befdcc22

2022-06-03T09:28:17.000Z

sentence-similarity

kimcando

kimcando/ko-paraKQC-demo2

sentence-transformers

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # kimcando/ko-paraKQC-demo2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('kimcando/ko-paraKQC-demo2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('kimcando/ko-paraKQC-demo2') model = AutoModel.from_pretrained('kimcando/ko-paraKQC-demo2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=kimcando/ko-paraKQC-demo2) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 475 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 190, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->

kleinay/qanom-seq2seq-model-order-invariant

6cd693fb61c167062b61ff2bf5dd125619b2e706

2022-06-03T09:50:24.000Z

text2text-generation

kleinay

kleinay/qanom-seq2seq-model-order-invariant

transformers

Entry not found

jppaolim/v47_Move2PT

648f9d0ed4c3d7d8fdc7a98dab1123adbd3f92fc

2022-06-03T12:23:52.000Z

text-generation

jppaolim

jppaolim/v47_Move2PT

transformers

# My Story model {'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} Arthur goes to the beach. Arthur wanted to go to the beach with his friends. Arthur wasn't a big fan of the beach. He asked his friend Steve to go to the beach with him. Steve brought a box of chips with Arthur's and him. Arthur and Steve had a great time at the beach. Arthur goes to the beach. Arthur loved to go to the beach. One day, his family decided to go to the beach. Arthur decided he would go with his family and swim. Arthur loved swimming so much that he asked his mother if he could go. His mother told him that she already had a place to stay. Arthur goes to the beach. Arthur was at the beach with friends. Arthur wanted to go to the beach. Arthur's friends thought that the beach was a bad place to go. However, Arthur decided to take his friends to the beach. The boys went to the beach. Arthur goes to the beach. Arthur was on a boat with his family. Arthur wanted to swim but he was afraid it would be stormy. The weatherman thought he may need to take a dip in the sand. Arthur reluctantly took a dip in the shallow. Arthur was glad to get back on the boat after all. Arthur goes to the beach. Arthur is out fishing. While fishing with his dad at the beach he hits a pothole. Arthur feels a painful pain. His dad picks him up and takes him to the doctor. Arthur is diagnosed with a broken leg. {'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1.05} Arthur goes to the beach. Arthur had never gone to the beach before. He went to the beach. While there he swam a few laps. He then went home and forgot about all of his work. Arthur felt better after that. Arthur goes to the beach. Arthur is out in the ocean with his friend. Arthur is walking along the shore when he sees something bad. Arthur starts to swim as fast as he can. He begins to feel bad and runs into the water. Arthur goes and rests on the shore in pain. Arthur goes to the beach. Arthur was having a great day on the beach. He was having so much fun! He wanted to go to the beach. Arthur got in his car and drove to the beach. Arthur was happy he had spent the day on the beach. Arthur goes to the beach. Arthur wanted to see the ocean. He didn't have a boat. Arthur decided to take a boat to the ocean. Arthur went on the water with his boat. Arthur and his friends went on the water. Arthur goes to the beach. Arthur had never been to the beach before. He had never been to one before so he went with his friends. Arthur and his friends met up at a local hotel. Arthur got a towel and started walking toward the beach. Arthur was happy that he had finally visited the beach. {'top_p': 0.9, 'top_k': 40, 'temperature': 0.8, 'repetition_penalty': 1.1} Arthur goes to the beach. Arthur is out on a relaxing day at the beach with his family. Arthur is enjoying the relaxing sun. Arthur spots an injured whale. Arthur takes the whale to the shore. Arthur and his family go home and relax. Arthur goes to the beach. Arthur is going to the beach. He wants to go swimming. Arthur gets a towel. Arthur goes swimming. Arthur swims the whole day. Arthur goes to the beach. Arthur wanted to go swimming with his friends. They decided to go to the beach. Arthur's friends told him to go swimming. Arthur went swimming but fell and skinned his face! He had a terrible day at the beach! Arthur goes to the beach. Arthur had never been on a beach before. He decided to take a chance and decided to go to the beach. Arthur went to the beach and saw many beautiful waves. He had a great time at the beach. Arthur decided that he would try another beach day. Arthur goes to the beach. Arthur was swimming with his friends. He wanted to go swimming but had no money. He decided to get some food from the beach. Arthur brought his friend along with him. Arthur spent all of his time in the ocean. {'top_p': 0.9, 'top_k': 40, 'temperature': 0.6, 'repetition_penalty': 1.15} Arthur goes to the beach. Arthur wanted to go on a vacation. He decided to go to the beach. At first he was very nervous. However, he enjoyed the water and the ocean. Arthur was glad he went on such a relaxing vacation. Arthur goes to the beach. Arthur was at the beach with his friends. He decided he wanted a fun day on the water. Arthur decided to go for a swim in the ocean. Arthur swam in the ocean for an hour. Arthur had a great time at the beach with his friends. Arthur goes to the beach. Arthur was out on a boat with his friends. They were on their way to the beach. Arthur saw that there was a huge jellyfish in the water. He swam to save it and catch it. His friends loved to eat jellyfish! Arthur goes to the beach. Arthur was going on a long trip to the beach. He had never been before. He decided to go with his friends. Arthur and his friends went to the beach. Arthur was happy to have a new adventure. Arthur goes to the beach. Arthur was going on a vacation. He went out to the water. Arthur was swimming and relaxing in the ocean. Arthur felt really cool. Arthur decided he would go back to the beach. {'top_p': 0.9, 'top_k': 40, 'temperature': 0.4, 'repetition_penalty': 1.2} Arthur goes to the beach. Arthur is going to go to the beach. He has a lot of fun at the beach. He gets in his car and drives to the beach. He spends a few hours at the beach. Arthur is happy he went to the beach. Arthur goes to the beach. Arthur was going on a vacation with his family. He decided he wanted to go to the beach. Arthur and his family packed up their bags and headed out. Arthur took a long walk down the beach. Afterwards, Arthur and his family went for a swim in the ocean. Arthur goes to the beach. Arthur is a young boy who loves going on vacation. He decides he wants to go on a trip to the ocean. Arthur takes his friend Tom with him to the beach. Tom and Arthur spend the whole day swimming in the water. Arthur is happy that he went on a vacation to the ocean. Arthur goes to the beach. Arthur is out on a boat. He has been fishing for hours. Arthur feels tired and cannot sleep. He decides to go to the beach. Arthur enjoys his day at the beach. Arthur goes to the beach. Arthur was on a trip with his family. He decided to go to the beach. Arthur was very excited. Arthur went out and bought a surfboard. Arthur was happy he went to the beach.

roshnir/xlmr-base-trained-squadv2

aafdfd9407133b0685d111db39ed3bcaa38651d9

2022-06-03T13:20:24.000Z

question-answering

roshnir

roshnir/xlmr-base-trained-squadv2

transformers

Entry not found

huggingtweets/washirerpadvice

0a492396b809704404760775ae978e910b1143e4

2022-06-03T13:29:32.000Z

text-generation

huggingtweets

huggingtweets/washirerpadvice

transformers

--- language: en thumbnail: http://www.huggingtweets.com/washirerpadvice/1654262967962/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1381256890542387204/zaT8DfFD_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Washire RP Tips</div> <div style="text-align: center; font-size: 14px;">@washirerpadvice</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Washire RP Tips. | Data | Washire RP Tips | | --- | --- | | Tweets downloaded | 243 | | Retweets | 4 | | Short tweets | 5 | | Tweets kept | 234 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/gq82nlvl/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @washirerpadvice's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/325ay6n9) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/325ay6n9/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/washirerpadvice') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

kaouther/distilbert-base-uncased-finetuned-squad

c4bbeb925cad3f85b53cac57ca7f303800a6dece

2022-06-03T15:29:20.000Z

question-answering

kaouther

kaouther/distilbert-base-uncased-finetuned-squad

transformers

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1703 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2166 | 1.0 | 5533 | 1.1583 | | 0.9572 | 2.0 | 11066 | 1.1387 | | 0.7377 | 3.0 | 16599 | 1.1703 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

huggingtweets/calamitiddy

42f185a6caef9ceca6e074de16526963cf3882f3

2022-06-03T14:07:14.000Z

text-generation

huggingtweets

huggingtweets/calamitiddy

transformers

--- language: en thumbnail: http://www.huggingtweets.com/calamitiddy/1654265229643/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1532055379688841216/qJTjpsoB_400x400.png&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">lauren rhiannon (nail cleanup duty)</div> <div style="text-align: center; font-size: 14px;">@calamitiddy</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from lauren rhiannon (nail cleanup duty). | Data | lauren rhiannon (nail cleanup duty) | | --- | --- | | Tweets downloaded | 3204 | | Retweets | 1654 | | Short tweets | 164 | | Tweets kept | 1386 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3bl5mrs4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @calamitiddy's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fnxzf4e2) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fnxzf4e2/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/calamitiddy') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

jppaolim/v49Neo

4cf262ad19d8aac85a4194494dfde7615d24d245

2022-06-03T16:34:46.000Z

text-generation

jppaolim

jppaolim/v49Neo

transformers

# My Story model {'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} Arthur goes to the beach. Arthur was bored today. He took a vacation to the beach. The beach was very crowded. Arthur finally enjoyed the beach for the beach. He had so much fun he decided to take his vacation there. Arthur goes to the beach. Arthur was walking down the street one day and heard a loud boom. A huge shark had been spotted and was heading towards him! He ran to the beach and immediately jumped in the water. He swam to shore with his surfboard and his surf trunks. After five minutes of not paying attention, he got out of the water. Arthur goes to the beach. Arthur always loved going to the beach. His favorite thing to do in the morning was go to the beach. He decided he wanted to go to the beach, not too long. Arthur packed up his backpack and headed towards the beach. He started to enjoy himself as he was going to the beach, he loved it. Arthur goes to the beach. Arthur had always loved going to the beach. His friend told him to take the bus. Arthur forgot to bring his wallet. He was disappointed to see that his friend was gone. Arthur decided to leave the beach without taking the bus. Arthur goes to the beach. Arthur wanted to visit the beach but his parents didn't take him. His parents thought that his parents should take him. They bought him a beach chair and took him to the beach. He had a great time, but the beach wasn't too bad. Arthur was very disappointed to see no sand at all! {'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1.05} Arthur goes to the beach. Arthur is in the ocean. He swims for an hour. He feels great. He goes home. He goes swimming again. Arthur goes to the beach. Arthur was on vacation with his family He had a very nice day at the beach. As he was driving to the beach he saw a beautiful view. He quickly started to relax as he got closer to the beach. It turned out that he was sitting down at the beach by his family. Arthur goes to the beach. Arthur is always very worried about it. He has always been afraid of going to the beach. One day he has no idea what's going to happen. He decides to take a trip. He cannot believe he is going to the beach. Arthur goes to the beach. Arthur wanted to learn how to surf. So he took out his surf equipment. He put his surf equipment on. He set his surfboard up and put it on the beach. Arthur had a great time surfing! Arthur goes to the beach. Arthur loved the outdoors. He wanted to go in the water. He was very bored one day. Arthur was going to the beach. He spent the whole day swimming and sunbathing. {'top_p': 0.9, 'top_k': 40, 'temperature': 0.8, 'repetition_penalty': 1.1} Arthur goes to the beach. Arthur was going to the beach. He went to the beach and swam. He went to the beach and swam in the water. He fell in the water and was wet. Arthur never went to the beach again. Arthur goes to the beach. Arthur is bored. He heads to the beach. Arthur sits down on the sand. He runs to the beach. Arthur swam in the water. Arthur goes to the beach. Arthur was on vacation. He decided to go to the beach. He went to the beach and played on the sand. He felt very hot and cold. Arthur spent the entire day at the beach. Arthur goes to the beach. Arthur was very excited to go to the beach with his friends. His friends were already at the beach. He was going to be at the beach on his birthday. He got all his friends together and had a great time. He was glad he had a great time and decided to go home. Arthur goes to the beach. Arthur was walking down the street. He was heading to the beach. He was going to swim with his friends. They were going to take him to the water. Arthur had a great time swimming. {'top_p': 0.9, 'top_k': 40, 'temperature': 0.6, 'repetition_penalty': 1.15} Arthur goes to the beach. Arthur is very bored. He spends all day sitting on the sand. He decides to go to the beach. He spends all day swimming. Arthur is happy he went to the beach. Arthur goes to the beach. Arthur is walking down the street. He sees a big wave. He runs to the side of the road. He trips and falls in the water. Arthur is shaken up by the wave. Arthur goes to the beach. Arthur is on his way to the beach. He has never been in the beach before. He decides to go for a walk. While walking he falls in the water. Arthur is soaked and had to go home. Arthur goes to the beach. Arthur was a little boy. He loved to surf, but he didn't know how to swim. His mom took him to the beach. He swam in the water and got very cold. Arthur spent all day in the sand and had a good time. Arthur goes to the beach. Arthur was very bored. He was in his car. He drove to the beach. He went to the beach. He went to the beach and played with the beach. {'top_p': 0.9, 'top_k': 40, 'temperature': 0.4, 'repetition_penalty': 1.2} Arthur goes to the beach. Arthur was going to the beach with his family. He was going to take a nice walk on the sand. He was going to take a nice long stroll. He saw a huge wave and decided to go for it. He had a great time on the beach. Arthur goes to the beach. Arthur is going to the beach with his friends. He is going to take a few hours to get there. He is going to go to the beach and surf. He is going to surf for the first time. He is excited to go to the beach and surf. Arthur goes to the beach. Arthur is going to the beach. He is going to swim in the water. He is going to go for a quick walk. Arthur is not able to walk. Arthur is late for his appointment. Arthur goes to the beach. Arthur is going to the beach. He is going to go swimming. Arthur is going to go swimming with his friends. He is going to swim with his friends. Arthur is very excited for the beach trip. Arthur goes to the beach. Arthur is a very good swimmer. He has always been very careful with his swimming. One day, he decides to go to the beach. While at the beach, he swam for hours. Finally, he was able to get to the beach safely.