Hugging Face's logo

hung200504
/
bert-covid

Question Answering
Transformers
PyTorch
bert
Generated from Trainer
Inference Endpoints
Model card Files Community
bert-covid / README.md
hung200504's picture
hung200504
bert-squad
b8968a7
preview code
|
raw history blame contribute delete
No virus
4.85 kB
metadata
license: mit
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
tags:
  - generated_from_trainer
datasets:
  - covid_qa_deepset
model-index:
  - name: bert-covid
    results: []

bert-covid

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the covid_qa_deepset dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6900

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
5.474 0.04 5 4.3730
3.9933 0.09 10 3.2783
3.0206 0.13 15 2.0289
1.9741 0.18 20 1.3879
1.4351 0.22 25 1.1733
1.5916 0.26 30 1.1623
0.5383 0.31 35 1.1952
0.7776 0.35 40 1.1920
1.1785 0.39 45 1.1216
1.1334 0.44 50 1.0412
0.7445 0.48 55 1.0829
0.6512 0.53 60 1.0443
0.7516 0.57 65 1.0089
0.5953 0.61 70 0.9273
0.8589 0.66 75 0.8947
0.7561 0.7 80 0.9009
0.9561 0.75 85 0.9006
0.7731 0.79 90 0.8482
0.8269 0.83 95 0.8380
0.9884 0.88 100 0.8200
0.9187 0.92 105 0.8775
0.585 0.96 110 0.8499
0.6835 1.01 115 0.8314
0.6668 1.05 120 0.7491
0.5558 1.1 125 0.7154
0.4491 1.14 130 0.8212
1.0667 1.18 135 0.8477
0.4472 1.23 140 0.7636
0.6892 1.27 145 0.7493
0.66 1.32 150 0.6932
0.5044 1.36 155 0.7675
0.5329 1.4 160 0.7406
0.2223 1.45 165 0.8099
0.5495 1.49 170 0.8758
0.5534 1.54 175 0.8476
0.4962 1.58 180 0.7953
0.7477 1.62 185 0.7610
0.7293 1.67 190 0.8357
0.6205 1.71 195 0.7339
0.5687 1.75 200 0.6908
0.884 1.8 205 0.6706
0.5928 1.84 210 0.6546
0.3209 1.89 215 0.6505
0.7585 1.93 220 0.6486
0.8501 1.97 225 0.6272
0.1664 2.02 230 0.6211
0.4483 2.06 235 0.6550
0.3361 2.11 240 0.6604
0.3085 2.15 245 0.6520
0.2407 2.19 250 0.6695
0.3418 2.24 255 0.6687
0.3165 2.28 260 0.6730
0.5811 2.32 265 0.6546
0.3516 2.37 270 0.6579
0.3136 2.41 275 0.6688
0.2508 2.46 280 0.6921
0.3463 2.5 285 0.7124
0.3603 2.54 290 0.7160
0.4455 2.59 295 0.6995
0.5433 2.63 300 0.6919
0.3411 2.68 305 0.6898
0.6065 2.72 310 0.6922
0.6258 2.76 315 0.6955
0.283 2.81 320 0.7008
0.6233 2.85 325 0.6988
0.3899 2.89 330 0.6949
0.238 2.94 335 0.6916
0.3166 2.98 340 0.6900

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1