---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BERT-evidence-types
  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. -->

# BERT-evidence-types

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4683
- Macro f1: 0.3854
- Weighted f1: 0.6985
- Accuracy: 0.7116
- Balanced accuracy: 0.3720

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:|
| 1.1736        | 1.0   | 125  | 1.0715          | 0.2535   | 0.6525      | 0.6667   | 0.2721            |
| 0.8182        | 2.0   | 250  | 0.9876          | 0.3068   | 0.6769      | 0.6804   | 0.3152            |
| 0.6024        | 3.0   | 375  | 1.0436          | 0.4067   | 0.7024      | 0.7047   | 0.4089            |
| 0.4004        | 4.0   | 500  | 1.1912          | 0.4045   | 0.6988      | 0.7078   | 0.4050            |
| 0.2531        | 5.0   | 625  | 1.3408          | 0.3882   | 0.6861      | 0.6903   | 0.3967            |
| 0.1625        | 6.0   | 750  | 1.5378          | 0.3866   | 0.6951      | 0.7040   | 0.3807            |
| 0.0985        | 7.0   | 875  | 1.7579          | 0.3850   | 0.6990      | 0.7161   | 0.3824            |
| 0.0664        | 8.0   | 1000 | 1.9837          | 0.3609   | 0.6849      | 0.7032   | 0.3529            |
| 0.0411        | 9.0   | 1125 | 2.0033          | 0.3807   | 0.6929      | 0.7024   | 0.3618            |
| 0.0262        | 10.0  | 1250 | 2.1714          | 0.3771   | 0.6924      | 0.7085   | 0.3585            |
| 0.0204        | 11.0  | 1375 | 2.2539          | 0.3734   | 0.6832      | 0.6933   | 0.3658            |
| 0.0141        | 12.0  | 1500 | 2.3033          | 0.3654   | 0.6830      | 0.6979   | 0.3556            |
| 0.0118        | 13.0  | 1625 | 2.3853          | 0.3679   | 0.6912      | 0.7108   | 0.3520            |
| 0.0109        | 14.0  | 1750 | 2.3749          | 0.3810   | 0.6952      | 0.7100   | 0.3665            |
| 0.0078        | 15.0  | 1875 | 2.4042          | 0.3777   | 0.6942      | 0.7078   | 0.3645            |
| 0.0079        | 16.0  | 2000 | 2.5097          | 0.3790   | 0.6938      | 0.7123   | 0.3632            |
| 0.0073        | 17.0  | 2125 | 2.4305          | 0.3844   | 0.6957      | 0.7070   | 0.3725            |
| 0.0046        | 18.0  | 2250 | 2.4700          | 0.3762   | 0.6941      | 0.7093   | 0.3638            |
| 0.0064        | 19.0  | 2375 | 2.4566          | 0.3844   | 0.6974      | 0.7100   | 0.3713            |
| 0.0057        | 20.0  | 2500 | 2.4683          | 0.3854   | 0.6985      | 0.7116   | 0.3720            |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1