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--- |
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license: apache-2.0 |
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base_model: distilbert/distilbert-base-uncased |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: text-pic-request-identifier |
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results: [] |
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datasets: |
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- andriadze/pic-text-requests-synth |
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widget: |
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- text: "I'd love to see that" |
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output: |
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- label: pic |
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score: 0.99 |
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- label: text |
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score: 0.01 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# text-pic-request-identifier |
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an synthetic dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0015 |
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- Accuracy: 0.9996 |
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## Model description |
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Model identifies if user is asking for a picture or a text. |
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## Intended uses & limitations |
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Intended use for chat applications to either route the message to a text model or an image model. |
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Model will return 'pic' or 'text' |
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## Training and evaluation data |
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Model was trained on synthetic dataset consisting of around ~25k messages. Messages were generated by different LLM's including gpt4,gpt4o,gpt4o-mini,gpt3.5-turbo |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.0391 | 1.0 | 844 | 0.0021 | 0.9996 | |
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| 0.0021 | 2.0 | 1688 | 0.0015 | 0.9996 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.3.1 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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### How to use |
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```python |
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from transformers import ( |
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pipeline |
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) |
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picClassifier = pipeline("text-classification", model="andriadze/text-pic-request-identifier") |
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res = picClassifier('Can you send me a selfie?') |
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``` |
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