Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+base_model: manuel-couto-pintos/roberta_erisk
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:50881
+- loss:TripletLoss
+widget:
+- source_sentence: I smoked weed for the first time ever a couple days ago, how long
+    until it's out of my system?
+  sentences:
+  - If I haven't smoked weed in a long time and smoked 1 day, how long will it be
+    in my urine?
+  - Where can we find best delay pedal?
+  - How long does it take for an avid weed smoker to pass a urine drug test?
+- source_sentence: What are the visiting places in coorg?
+  sentences:
+  - How can I find a co-working space in Gurgaon?
+  - What are the places to visit in coorg?
+  - What are your favourite celebrity cookbooks?
+- source_sentence: What is the best used car to get under 5k?
+  sentences:
+  - What's the best used car for under 5k?
+  - What do you think about RBI's new move of banning 500 and 1000 notes?
+  - Which is the best car to buy under 6 lakhs?
+- source_sentence: Which exercises can I do at home to reduce belly fat?
+  sentences:
+  - What exercise we can do to reduce belly fat at home?
+  - What is a first time home buyer?
+  - My upper body is in shape but my thighs are very fatty and big ...so how can I
+    reduce my thighs .I am doing running of 3km daily only?
+- source_sentence: Which is the best affiliate program?
+  sentences:
+  - How can I learn to make good coffee at home?
+  - What are the best affiliate networks in the UK?
+  - What are the best affiliate programs?
+---
+
+# SentenceTransformer based on manuel-couto-pintos/roberta_erisk
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [manuel-couto-pintos/roberta_erisk](https://huggingface.co/manuel-couto-pintos/roberta_erisk). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [manuel-couto-pintos/roberta_erisk](https://huggingface.co/manuel-couto-pintos/roberta_erisk) <!-- at revision 9aa8180ee595fe69a8d23c06dc5ee405f4f5d5ac -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("manuel-couto-pintos/roberta_erisk_sts")
+# Run inference
+sentences = [
+    'Which is the best affiliate program?',
+    'What are the best affiliate programs?',
+    'What are the best affiliate networks in the UK?',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 50,881 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                        | sentence_2                                                                        |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 13.77 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.82 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.96 tokens</li><li>max: 59 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                     | sentence_1                                                          | sentence_2                                                                                                    |
+  |:---------------------------------------------------------------|:--------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|
+  | <code>What is a good definition of Quora?</code>               | <code>What is the best definition of Quora?</code>                  | <code>What is Quora address?</code>                                                                           |
+  | <code>How can I make myself appear offline on facebook?</code> | <code>How do you make sure to appear as offline on Facebook?</code> | <code>How can I get Facebook to remember to keep chat offline?</code>                                         |
+  | <code>How do I gain some healthy weight?</code>                | <code>What is the best way for underweight to gain weight?</code>   | <code>My boyfriend doesn't eat a lot. What are some ways to help him gain weight fast? He's 5'7 120lbs</code> |
+* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
+      "triplet_margin": 5
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 10
+- `per_device_eval_batch_size`: 10
+- `num_train_epochs`: 10
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 10
+- `per_device_eval_batch_size`: 10
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0983 | 500   | 4.3807        |
+| 0.1965 | 1000  | 2.5872        |
+| 0.2948 | 1500  | 1.7484        |
+| 0.3930 | 2000  | 1.2649        |
+| 0.4913 | 2500  | 1.0219        |
+| 0.5895 | 3000  | 0.8703        |
+| 0.6878 | 3500  | 0.771         |
+| 0.7860 | 4000  | 0.655         |
+| 0.8843 | 4500  | 0.6547        |
+| 0.9825 | 5000  | 0.5772        |
+| 1.0808 | 5500  | 0.5628        |
+| 1.1790 | 6000  | 0.5163        |
+| 1.2773 | 6500  | 0.4871        |
+| 1.3755 | 7000  | 0.4842        |
+| 1.4738 | 7500  | 0.4316        |
+| 1.5720 | 8000  | 0.4199        |
+| 1.6703 | 8500  | 0.3554        |
+| 1.7685 | 9000  | 0.3467        |
+| 1.8668 | 9500  | 0.3591        |
+| 1.9650 | 10000 | 0.3356        |
+| 2.0633 | 10500 | 0.3281        |
+| 2.1615 | 11000 | 0.3149        |
+| 2.2598 | 11500 | 0.2767        |
+| 2.3580 | 12000 | 0.2849        |
+| 2.4563 | 12500 | 0.244         |
+| 2.5545 | 13000 | 0.2416        |
+| 2.6528 | 13500 | 0.2008        |
+| 2.7510 | 14000 | 0.1718        |
+| 2.8493 | 14500 | 0.188         |
+| 2.9475 | 15000 | 0.1656        |
+| 3.0458 | 15500 | 0.1522        |
+| 3.1440 | 16000 | 0.144         |
+| 3.2423 | 16500 | 0.1329        |
+| 3.3405 | 17000 | 0.1431        |
+| 3.4388 | 17500 | 0.128         |
+| 3.5370 | 18000 | 0.1251        |
+| 3.6353 | 18500 | 0.0921        |
+| 3.7335 | 19000 | 0.0882        |
+| 3.8318 | 19500 | 0.1087        |
+| 3.9300 | 20000 | 0.0819        |
+| 4.0283 | 20500 | 0.0916        |
+| 4.1265 | 21000 | 0.0837        |
+| 4.2248 | 21500 | 0.0855        |
+| 4.3230 | 22000 | 0.0727        |
+| 4.4213 | 22500 | 0.0772        |
+| 4.5196 | 23000 | 0.0676        |
+| 4.6178 | 23500 | 0.0597        |
+| 4.7161 | 24000 | 0.0555        |
+| 4.8143 | 24500 | 0.0613        |
+| 4.9126 | 25000 | 0.0589        |
+| 5.0108 | 25500 | 0.0503        |
+| 5.1091 | 26000 | 0.0546        |
+| 5.2073 | 26500 | 0.0446        |
+| 5.3056 | 27000 | 0.0591        |
+| 5.4038 | 27500 | 0.0431        |
+| 5.5021 | 28000 | 0.0402        |
+| 5.6003 | 28500 | 0.0354        |
+| 5.6986 | 29000 | 0.0405        |
+| 5.7968 | 29500 | 0.0308        |
+| 5.8951 | 30000 | 0.0363        |
+| 5.9933 | 30500 | 0.0365        |
+| 6.0916 | 31000 | 0.0333        |
+| 6.1898 | 31500 | 0.0238        |
+| 6.2881 | 32000 | 0.0372        |
+| 6.3863 | 32500 | 0.0331        |
+| 6.4846 | 33000 | 0.0253        |
+| 6.5828 | 33500 | 0.0315        |
+| 6.6811 | 34000 | 0.0193        |
+| 6.7793 | 34500 | 0.0239        |
+| 6.8776 | 35000 | 0.0201        |
+| 6.9758 | 35500 | 0.0213        |
+| 7.0741 | 36000 | 0.0187        |
+| 7.1723 | 36500 | 0.0125        |
+| 7.2706 | 37000 | 0.0151        |
+| 7.3688 | 37500 | 0.0208        |
+| 7.4671 | 38000 | 0.0101        |
+| 7.5653 | 38500 | 0.0191        |
+| 7.6636 | 39000 | 0.0125        |
+| 7.7618 | 39500 | 0.0136        |
+| 7.8601 | 40000 | 0.0135        |
+| 7.9583 | 40500 | 0.0118        |
+| 8.0566 | 41000 | 0.012         |
+| 8.1548 | 41500 | 0.0079        |
+| 8.2531 | 42000 | 0.0105        |
+| 8.3513 | 42500 | 0.0094        |
+| 8.4496 | 43000 | 0.0079        |
+| 8.5478 | 43500 | 0.0118        |
+| 8.6461 | 44000 | 0.0105        |
+| 8.7444 | 44500 | 0.0058        |
+| 8.8426 | 45000 | 0.013         |
+| 8.9409 | 45500 | 0.0065        |
+| 9.0391 | 46000 | 0.0089        |
+| 9.1374 | 46500 | 0.0031        |
+| 9.2356 | 47000 | 0.008         |
+| 9.3339 | 47500 | 0.0065        |
+| 9.4321 | 48000 | 0.0052        |
+| 9.5304 | 48500 | 0.0066        |
+| 9.6286 | 49000 | 0.0039        |
+| 9.7269 | 49500 | 0.004         |
+| 9.8251 | 50000 | 0.0051        |
+| 9.9234 | 50500 | 0.003         |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.0.1
+- Transformers: 4.44.2
+- PyTorch: 2.0.1+cu117
+- Accelerate: 0.32.0
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### TripletLoss
+```bibtex
+@misc{hermans2017defense,
+    title={In Defense of the Triplet Loss for Person Re-Identification}, 
+    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
+    year={2017},
+    eprint={1703.07737},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "manuel-couto-pintos/roberta_erisk",
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "RobertaTokenizerFast",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.0.1+cu117"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cd182654ae1388dcdde117c6ce23e860d27f17777530978c7fc087aa7cc2fd69
+size 498604904

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}