armanc/scientific_papers
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How to use David-Xu/my_awesome_arxiv_model with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("David-Xu/my_awesome_arxiv_model")
model = AutoModelForSeq2SeqLM.from_pretrained("David-Xu/my_awesome_arxiv_model", device_map="auto")This model is a fine-tuned version of t5-small on the scientific_papers dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.8985 | 1.0 | 1269 | 2.6663 | 0.1735 | 0.0651 | 0.1393 | 0.1393 | 19.0 |
| 2.7954 | 2.0 | 2538 | 2.6231 | 0.1759 | 0.0675 | 0.1417 | 0.1417 | 19.0 |
| 2.7799 | 3.0 | 3807 | 2.6054 | 0.1779 | 0.0674 | 0.1434 | 0.1435 | 19.0 |
| 2.7715 | 4.0 | 5076 | 2.5995 | 0.1783 | 0.0671 | 0.1433 | 0.1433 | 19.0 |
Base model
google-t5/t5-small