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license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Bioformer-LitCovid-v1.3h
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bioformer-LitCovid-v1.3h
This model is a fine-tuned version of [bioformers/bioformer-litcovid](https://huggingface.co/bioformers/bioformer-litcovid) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8951
- Hamming loss: 0.0168
- F1 micro: 0.8565
- F1 macro: 0.3960
- F1 weighted: 0.8831
- F1 samples: 0.8789
- Precision micro: 0.7903
- Precision macro: 0.3221
- Precision weighted: 0.8426
- Precision samples: 0.8631
- Recall micro: 0.9348
- Recall macro: 0.6915
- Recall weighted: 0.9348
- Recall samples: 0.9435
- Roc Auc: 0.9604
- Accuracy: 0.6896
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 3257
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Hamming loss | F1 micro | F1 macro | F1 weighted | F1 samples | Precision micro | Precision macro | Precision weighted | Precision samples | Recall micro | Recall macro | Recall weighted | Recall samples | Roc Auc | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 1.2033 | 1.0 | 2272 | 0.5628 | 0.0616 | 0.6107 | 0.2167 | 0.7918 | 0.7257 | 0.4618 | 0.1789 | 0.7347 | 0.6771 | 0.9014 | 0.7310 | 0.9014 | 0.9194 | 0.9209 | 0.3870 |
| 1.2127 | 2.0 | 4544 | 0.5062 | 0.0325 | 0.7555 | 0.2834 | 0.8357 | 0.8037 | 0.6337 | 0.2273 | 0.7680 | 0.7535 | 0.9353 | 0.7100 | 0.9353 | 0.9434 | 0.9523 | 0.4954 |
| 0.96 | 3.0 | 6816 | 0.4943 | 0.0245 | 0.8043 | 0.3363 | 0.8608 | 0.8409 | 0.7043 | 0.2676 | 0.8069 | 0.8048 | 0.9372 | 0.7637 | 0.9372 | 0.9477 | 0.9575 | 0.5735 |
| 0.5852 | 4.0 | 9088 | 0.7306 | 0.0195 | 0.8371 | 0.3860 | 0.8687 | 0.8624 | 0.7568 | 0.3083 | 0.8212 | 0.8378 | 0.9365 | 0.7232 | 0.9365 | 0.9459 | 0.9597 | 0.6410 |
| 0.3454 | 5.0 | 11360 | 0.8951 | 0.0168 | 0.8565 | 0.3960 | 0.8831 | 0.8789 | 0.7903 | 0.3221 | 0.8426 | 0.8631 | 0.9348 | 0.6915 | 0.9348 | 0.9435 | 0.9604 | 0.6896 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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