---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: gpt2-finetuned-academic-topics
  results: []
---

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# gpt2-finetuned-academic-topics

This model is a fine-tuned version of [gpt2](https://huggingface.co/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