license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: praveenseb/product_review_generator
results: []
datasets:
- amazon_us_reviews
pipeline_tag: text-generation
praveenseb/product_review_generator
This model is a fine-tuned version of distilgpt2 on a sample of amazon_us_reviews dataset. The sample was drawn from 'Apparel_v1_00' subset.
Model description
This model can auto generate review text for apparel products on providing product title, review rating (1-5) and review headline as an input prompt.
The input prompt should be in the format <|BOS|>product_title<|SEP|>product_rating<|SEP|>review_title<|SEP|>. For example, <|BOS|>Columbia Women's Benton Springs Full-Zip Fleece Jacket<|SEP|>5<|SEP|>Awesome jacket!<|SEP|>. You can find the complete code in my GitHub repository.
Intended uses & limitations
This model is only intended to demonstrate the text generation capabilities of transformer-based models. Do not use it commercially or for any real-life purpose . The model is trained specifically on 'Apparel_v1_00' dataset. So, using non-apparel product titles in the input prompt may yield inconsistent results.
Training procedure
Code used for training can found in my GitHub repository.
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'ExponentialDecay', 'config': {'initial_learning_rate': 0.0002, 'decay_steps': 1000, 'decay_rate': 0.95, 'staircase': True, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.0}
- training_precision: float32
Training results
| Train Loss | Epoch |
|---|---|
| 0.7579 | 0 |
| 0.6720 | 1 |
Framework versions
- Transformers 4.27.3
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2