---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioLinkBERT-Large-LitCovid-v1.3.1c
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# BioLinkBERT-Large-LitCovid-v1.3.1c

This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1423
- Hamming loss: 0.0115
- F1 micro: 0.8955
- F1 macro: 0.5189
- F1 weighted: 0.8999
- F1 samples: 0.9001
- Precision micro: 0.8699
- Precision macro: 0.4571
- Precision weighted: 0.8797
- Precision samples: 0.8987
- Recall micro: 0.9228
- Recall macro: 0.6461
- Recall weighted: 0.9228
- Recall samples: 0.9346
- Roc Auc: 0.9575
- Accuracy: 0.7369

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 2.3002        | 1.0   | 9086  | 0.8219          | 0.0155       | 0.8654   | 0.4167   | 0.8761      | 0.8797     | 0.8090          | 0.3554          | 0.8345             | 0.8627            | 0.9302       | 0.6257       | 0.9302          | 0.9414         | 0.9589  | 0.6796   |
| 1.273         | 2.0   | 18172 | 0.7056          | 0.0145       | 0.8730   | 0.4396   | 0.8878      | 0.8898     | 0.8208          | 0.3638          | 0.8526             | 0.8772            | 0.9323       | 0.6919       | 0.9323          | 0.9439         | 0.9604  | 0.7050   |
| 0.8734        | 3.0   | 27258 | 0.8218          | 0.0123       | 0.8896   | 0.4846   | 0.8969      | 0.8958     | 0.8557          | 0.4219          | 0.8729             | 0.8893            | 0.9264       | 0.6671       | 0.9264          | 0.9378         | 0.9588  | 0.7249   |
| 0.7889        | 4.0   | 36344 | 0.9218          | 0.0118       | 0.8931   | 0.5037   | 0.9001      | 0.8983     | 0.8651          | 0.4391          | 0.8810             | 0.8966            | 0.9230       | 0.6716       | 0.9230          | 0.9349         | 0.9574  | 0.7333   |
| 0.4284        | 5.0   | 45430 | 1.1423          | 0.0115       | 0.8955   | 0.5189   | 0.8999      | 0.9001     | 0.8699          | 0.4571          | 0.8797             | 0.8987            | 0.9228       | 0.6461       | 0.9228          | 0.9346         | 0.9575  | 0.7369   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3