---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: PubMedBERT-Large-LitCovid-1.4
  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. -->

# PubMedBERT-Large-LitCovid-1.4

This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-large-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-large-uncased-abstract) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6105
- Hamming loss: 0.0623
- F1 micro: 0.6724
- F1 macro: 0.5303
- F1 weighted: 0.7292
- F1 samples: 0.6741
- Precision micro: 0.5423
- Precision macro: 0.4146
- Precision weighted: 0.6499
- Precision samples: 0.5845
- Recall micro: 0.8849
- Recall macro: 0.8178
- Recall weighted: 0.8849
- Recall samples: 0.9022
- Roc Auc: 0.9133
- Accuracy: 0.1313

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.589         | 1.0   | 1151 | 0.5719          | 0.1031       | 0.5554   | 0.4307   | 0.6704      | 0.5629     | 0.4034          | 0.3213          | 0.5843             | 0.4435            | 0.8909       | 0.8673       | 0.8909          | 0.9062         | 0.8941  | 0.0363   |
| 0.4668        | 2.0   | 2302 | 0.5438          | 0.0836       | 0.6082   | 0.4623   | 0.6974      | 0.6147     | 0.4599          | 0.3478          | 0.6098             | 0.5052            | 0.8976       | 0.8556       | 0.8976          | 0.9123         | 0.9077  | 0.0774   |
| 0.3791        | 3.0   | 3453 | 0.5510          | 0.0790       | 0.6225   | 0.4829   | 0.7070      | 0.6247     | 0.4754          | 0.3661          | 0.6205             | 0.5140            | 0.9012       | 0.8541       | 0.9012          | 0.9165         | 0.9119  | 0.0759   |
| 0.307         | 4.0   | 4605 | 0.5954          | 0.0635       | 0.6688   | 0.5235   | 0.7280      | 0.6689     | 0.5371          | 0.4078          | 0.6477             | 0.5767            | 0.8863       | 0.8212       | 0.8863          | 0.9036         | 0.9134  | 0.1229   |
| 0.2687        | 5.0   | 5755 | 0.6105          | 0.0623       | 0.6724   | 0.5303   | 0.7292      | 0.6741     | 0.5423          | 0.4146          | 0.6499             | 0.5845            | 0.8849       | 0.8178       | 0.8849          | 0.9022         | 0.9133  | 0.1313   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.13.3