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: []
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