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gpt2-finetuned-academic-topics
This model is a fine-tuned version of gpt2 on a dataset of sequences of science, technology, engineering and mathematics academic topics/tags which a user has used on their CiteULike or Google Scholar profiles.
Please contact brichards88@uri.edu for questions or inquiries.
It achieves the following results on the evaluation set:
- Train Loss: 3.3216
- Validation Loss: 3.2215
- Epoch: 4
Model description
Give a sequence of topics, i.e.: "machine learning, deep learning, chemistry, evolution" the model will continue the sequence, effectively recommending/generating new topics that might be of interest.
Intended uses & limitations
The model is not guaranteed to generate a real topic or even a real word/words as output.
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 4.7873 | 4.2950 | 0 |
| 4.1032 | 3.8203 | 1 |
| 3.7363 | 3.5614 | 2 |
| 3.4999 | 3.3740 | 3 |
| 3.3216 | 3.2215 | 4 |
Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
