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--- |
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base_model: facebook/bart-large-mnli |
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datasets: |
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- reddgr/nli-chatbot-prompt-categorization |
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library_name: transformers |
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license: mit |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: zero-shot-prompt-classifier-bart-ft |
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results: [] |
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language: |
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- en |
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--- |
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# zero-shot-prompt-classifier-bart-ft |
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This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on the [reddgr/nli-chatbot-prompt-categorization](https://huggingface.co/datasets/reddgr/nli-chatbot-prompt-categorization) dataset. |
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The purpose of the model is to help classify chatbot prompts into categories that are relevant in the context of working with LLM conversational tools: |
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coding assistance, language assistance, role play, creative writing, general knowledge questions... |
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The model is fine-tuned and tested on the natural language inference (NLI) dataset [reddgr/nli-chatbot-prompt-categorization](https://huggingface.co/datasets/reddgr/nli-chatbot-prompt-categorization) |
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Below is a confusion matrix calculated on zero-shot inferences for the 10 most popular categories in the Test split of [reddgr/nli-chatbot-prompt-categorization](https://huggingface.co/datasets/reddgr/nli-chatbot-prompt-categorization) at the time of the first model upload. The classification with the base model on the same small test dataset is shown for comparison: |
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The current version of the fine-tuned model outperforms the base model [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) by 24 percentage points (60% accuracy vs 36% accuracy) in a test set with 10 candidate zero-shot classes (the most frequent categories in the test split of [reddgr/nli-chatbot-prompt-categorization](https://huggingface.co/datasets/reddgr/nli-chatbot-prompt-categorization)). |
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The chart below compares the results for the 12 most popular candidate classes in the Test split, where the base model's zero-shot accuracy is outperformed by 25 percentage points: |
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The dataset and the model are continously updated as they assist with content publishing on my website [Talking to Chatbots](https://talkingtochatbots) |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 5e-06, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} |
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- training_precision: float32 |
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### Training results |
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{'eval_loss': 0.8465692400932312, 'eval_runtime': 57.9011, 'eval_samples_per_second': 6.667, 'eval_steps_per_second': 0.846, 'epoch': 1.0, 'step': 19} |
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{'eval_loss': 0.8361125588417053, 'eval_runtime': 60.2437, 'eval_samples_per_second': 6.407, 'eval_steps_per_second': 0.813, 'epoch': 2.0, 'step': 38} |
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{'eval_loss': 0.6992325782775879, 'eval_runtime': 60.8204, 'eval_samples_per_second': 6.347, 'eval_steps_per_second': 0.806, 'epoch': 3.0, 'step': 57} |
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{'eval_loss': 0.8125494718551636, 'eval_runtime': 59.2043, 'eval_samples_per_second': 6.52, 'eval_steps_per_second': 0.828, 'epoch': 4.0, 'step': 76} |
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{'train_runtime': 1626.4598, 'train_samples_per_second': 1.424, 'train_steps_per_second': 0.047, 'total_flos': 624333153618216.0, 'train_loss': 0.7128369180779708, 'epoch': 4.0, 'step': 76} |
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Train metrics: {'train_runtime': 1626.4598, 'train_samples_per_second': 1.424, 'train_steps_per_second': 0.047, 'total_flos': 624333153618216.0, 'train_loss': 0.7128369180779708, 'epoch': 4.0} |
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### Framework versions |
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- Transformers 4.44.2 |
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- TensorFlow 2.18.0-dev20240717 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
