
<!-- 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. -->

# bert-finetuned-single-label-journal-classifier_not_quite_balanced

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:
- eval_loss: 0.4764
- eval_accuracy: 0.9135
- eval_f1: 0.9135
- eval_runtime: 6.8737
- eval_samples_per_second: 126.132
- eval_steps_per_second: 15.857
- epoch: 6.0
- step: 5838

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext", "model-index": [{"name": "bert-finetuned-single-label-journal-classifier_not_quite_balanced", "results": []}]}
transformers tensorboard safetensors bert text-classification generated_from_trainer base_model:microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext license:mit autotrain_compatible endpoints_compatible region:us