zero-shot-prompt-classifier-bart-ft

This model is a fine-tuned version of facebook/bart-large-mnli on the reddgr/nli-chatbot-prompt-categorization dataset.

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: coding assistance, language assistance, role play, creative writing, general knowledge questions...

The model is fine-tuned and tested on the natural language inference (NLI) dataset reddgr/nli-chatbot-prompt-categorization

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 at the time of the first model upload. The classification with the base model on the same small test dataset is shown for comparison:

The current version of the fine-tuned model outperforms the base model 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).

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:

The dataset and the model are continously updated as they assist with content publishing on my website Talking to Chatbots

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:

• 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}
• training_precision: float32

Training results

{'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} {'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} {'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} {'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} {'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} 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}

Framework versions

• Transformers 4.44.2
• TensorFlow 2.18.0-dev20240717
• Datasets 2.21.0
• Tokenizers 0.19.1