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--- |
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base_model: microsoft/Phi-3-mini-4k-instruct |
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library_name: transformers |
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license: mit |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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model-index: |
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- name: outputs |
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results: [] |
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--- |
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## Toxicity Classification Performance |
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Our merged model demonstrates exceptional performance on the toxicity classification task, outperforming several state-of-the-art language models. |
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### Classification Metrics |
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``` |
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precision recall f1-score support |
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0 0.85 0.90 0.87 175 |
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1 0.89 0.85 0.87 175 |
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accuracy 0.87 350 |
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macro avg 0.87 0.87 0.87 350 |
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weighted avg 0.87 0.87 0.87 350 |
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``` |
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### Model Usage |
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For best results, we recommend starting with the following prompting strategy (and encourage tweaks as you see fit): |
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```python |
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def format_input_classification(text): |
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input = f""" |
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You are examining written text content. Here is the text: |
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************ |
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[Text]: {text} |
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************ |
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Examine the text and determine whether the text is toxic or not. Toxicity is |
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defined as any comment that makes hateful statements, demeans or disparages |
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another user, uses inappropriate language, or threatens physical or emotional |
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violence. Please focus heavily on the concept of toxicity and do NOT mark |
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something toxic as non-toxic that is toxic. |
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Your classification must be single word followed by a reasoning as to why you chose |
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the classification, either "toxic" or "non-toxic". |
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"toxic" means that the text meets the definition of toxic. |
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"non-toxic" means the text does not contain any |
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words, sentiments or meaning that could be considered toxic. |
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After your classification, provide the reason for your classification. |
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""" |
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return input |
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text = format_input_classification("I could strangle him") |
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messages = [ |
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{"role": "user", "content": text} |
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] |
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pipe = pipeline( |
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"text-generation", |
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model=base_model, |
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model_kwargs={"attn_implementation": attn_implementation, "torch_dtype": torch.float16}, |
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tokenizer=tokenizer, |
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) |
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``` |
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Our model achieves an impressive precision of 0.85 for the toxic class and 0.89 for the non-toxic class, with a high overall accuracy of 0.87. The balanced F1-scores of 0.87 for both classes demonstrate the model's ability to handle this binary classification task effectively. |
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### Comparison with Other Models |
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| Model | Precision | Recall | F1 | |
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|-------------------|----------:|-------:|-------:| |
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| Our Merged Model | 0.85 | 0.90 | 0.87 | |
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| GPT-4 | 0.91 | 0.91 | 0.91 | |
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| GPT-4 Turbo | 0.89 | 0.77 | 0.83 | |
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| Gemini Pro | 0.81 | 0.84 | 0.83 | |
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| GPT-3.5 Turbo | 0.93 | 0.83 | 0.87 | |
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| Palm | - | - | - | |
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| Claude V2 | - | - | - | |
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[1] Scores from arize/phoenix |
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Compared to other language models, our merged model demonstrates competitive performance at a much smaller size, with a precision score of 0.85 and an F1 score of 0.87. |
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We will continue to refine and improve our merged model to achieve even better performance on model based toxicity evaluation tasks. |
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Citations: [1] https://docs.arize.com/phoenix/evaluation/how-to-evals/running-pre-tested-evals/retrieval-rag-relevance |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0009 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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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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- lr_scheduler_warmup_steps: 10 |
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- training_steps: 110 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
