---
base_model: facebook/bart-large-mnli
datasets:
- reddgr/nli-chatbot-prompt-categorization
library_name: transformers
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: zero-shot-prompt-classifier-bart-ft
  results: []
language:
- en
---

# zero-shot-prompt-classifier-bart-ft

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.

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](https://huggingface.co/datasets/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](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:

![Zero-shot prompt classification confusion matrix for reddgr/zero-shot-prompt-classifier-bart-ft](https://talkingtochatbots.com/wp-content/uploads/2025/01/zero-shot-prompt-classification-comparison-10-classes-60-accuracy.png)

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)).

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:

![Zero-shot prompt classification confusion matrix for reddgr/zero-shot-prompt-classifier-bart-ft](https://talkingtochatbots.com/wp-content/uploads/2025/01/zero-shot-prompt-classification-comparison-12-classes-57-accuracy.png)

The dataset and the model are continously updated as they assist with content publishing on my website [Talking to Chatbots](https://talkingtochatbots) 


## 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