---
license: mit
base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: BiomedNLP-CIViC-evidence-level-finetuned
  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. -->

# BiomedNLP-CIViC-evidence-level-finetuned

This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5745
- F1: 0.8312

## 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: 16
- eval_batch_size: 16
- 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 | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 73   | 1.5464          | 0.8260 |
| No log        | 2.0   | 146  | 1.7265          | 0.7844 |
| No log        | 3.0   | 219  | 1.6166          | 0.8260 |
| No log        | 4.0   | 292  | 1.5619          | 0.8260 |
| No log        | 5.0   | 365  | 1.5745          | 0.8312 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.21.0
- Tokenizers 0.15.0