farleyknight/big_patent_5_percent
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How to use farleyknight/patent-summarization-fb-bart-base-2022-09-20 with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("farleyknight/patent-summarization-fb-bart-base-2022-09-20")
model = AutoModelForSeq2SeqLM.from_pretrained("farleyknight/patent-summarization-fb-bart-base-2022-09-20", device_map="auto")This model is a fine-tuned version of facebook/bart-base on the farleyknight/big_patent_5_percent 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 |
|---|---|---|---|---|---|---|---|---|
| 3.0567 | 0.08 | 5000 | 2.8864 | 18.9387 | 7.1014 | 15.4506 | 16.8377 | 19.9979 |
| 2.9285 | 0.17 | 10000 | 2.7800 | 19.8983 | 7.3258 | 16.0823 | 17.7019 | 20.0 |
| 2.9252 | 0.25 | 15000 | 2.7080 | 19.6623 | 7.4627 | 16.0153 | 17.4485 | 20.0 |
| 2.8123 | 0.33 | 20000 | 2.6585 | 19.7414 | 7.5251 | 15.8166 | 17.4668 | 20.0 |
| 2.7117 | 0.41 | 25000 | 2.6070 | 19.7661 | 7.7193 | 16.2795 | 17.7884 | 20.0 |
| 2.7131 | 0.5 | 30000 | 2.5616 | 19.6706 | 7.4229 | 15.7998 | 17.4324 | 20.0 |
| 2.6373 | 0.58 | 35000 | 2.5250 | 20.0155 | 7.6811 | 16.1231 | 17.7578 | 20.0 |
| 2.6785 | 0.66 | 40000 | 2.4977 | 20.0974 | 7.9578 | 16.543 | 18.0242 | 20.0 |
| 2.6265 | 0.75 | 45000 | 2.4701 | 19.994 | 7.9114 | 16.3501 | 17.8786 | 20.0 |
| 2.5833 | 0.83 | 50000 | 2.4441 | 19.9981 | 7.934 | 16.3033 | 17.8674 | 20.0 |
| 2.5579 | 0.91 | 55000 | 2.4251 | 20.0544 | 7.8966 | 16.3889 | 17.9491 | 20.0 |
| 2.5242 | 0.99 | 60000 | 2.4097 | 20.1093 | 8.0572 | 16.4935 | 17.9823 | 20.0 |