---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: im-bin-tf-abstr
  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. -->

# im-bin-tf-abstr

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1908
- Accuracy: 0.9222
- F1: 0.9220
- Precision: 0.9267
- Recall: 0.9174
- Roc Auc: 0.9781

## 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: 1e-05
- train_batch_size: 640
- eval_batch_size: 1280
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| No log        | 1.0   | 375  | 0.2136          | 0.9124   | 0.9131 | 0.9087    | 0.9175 | 0.9733  |
| 0.3086        | 2.0   | 750  | 0.1971          | 0.9195   | 0.9190 | 0.9277    | 0.9104 | 0.9770  |
| 0.1917        | 3.0   | 1125 | 0.1908          | 0.9222   | 0.9220 | 0.9267    | 0.9174 | 0.9781  |
| 0.1791        | 4.0   | 1500 | 0.1909          | 0.9224   | 0.9224 | 0.9247    | 0.9202 | 0.9785  |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3