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 23 percentage points (57% accuracy vs 34% 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

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
0.9969	0.5490	0.9182	0.6225	0
0.7647	0.6601	1.0025	0.5441	1
0.6465	0.7157	1.1472	0.5392	2
0.5849	0.7418	1.1974	0.5049	3
0.4474	0.7843	1.5942	0.4657	4

Framework versions

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