pritamdeka/cord-19-abstract
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How to use pritamdeka/PubMedBert-abstract-cord19 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("fill-mask", model="pritamdeka/PubMedBert-abstract-cord19") # Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("pritamdeka/PubMedBert-abstract-cord19")
model = AutoModelForMaskedLM.from_pretrained("pritamdeka/PubMedBert-abstract-cord19")This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the pritamdeka/cord-19-abstract 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 |
|---|---|---|---|
| 1.3774 | 0.15 | 5000 | 1.3212 |
| 1.3937 | 0.29 | 10000 | 1.4059 |
| 1.6812 | 0.44 | 15000 | 1.6174 |
| 1.4712 | 0.59 | 20000 | 1.4383 |
| 1.4293 | 0.73 | 25000 | 1.4356 |
| 1.4155 | 0.88 | 30000 | 1.4283 |
| 1.3963 | 1.03 | 35000 | 1.4135 |
| 1.3718 | 1.18 | 40000 | 1.3948 |
| 1.369 | 1.32 | 45000 | 1.3961 |
| 1.354 | 1.47 | 50000 | 1.3788 |
| 1.3399 | 1.62 | 55000 | 1.3866 |
| 1.3289 | 1.76 | 60000 | 1.3630 |
| 1.3155 | 1.91 | 65000 | 1.3609 |
| 1.2976 | 2.06 | 70000 | 1.3489 |
| 1.2783 | 2.2 | 75000 | 1.3333 |
| 1.2696 | 2.35 | 80000 | 1.3260 |
| 1.2607 | 2.5 | 85000 | 1.3232 |
| 1.2547 | 2.64 | 90000 | 1.3034 |
| 1.2495 | 2.79 | 95000 | 1.3035 |
| 1.2404 | 2.94 | 100000 | 1.3029 |