---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: MiniLM-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. -->

# MiniLM-evidence-types

This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8388
- Macro f1: 0.4307
- Weighted f1: 0.6983
- Accuracy: 0.7032
- Balanced accuracy: 0.4139

## 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:|
| 1.3124        | 1.0   | 250  | 1.1166          | 0.2582   | 0.6393      | 0.6788   | 0.2758            |
| 0.9939        | 2.0   | 500  | 0.9671          | 0.3859   | 0.6988      | 0.7093   | 0.3799            |
| 0.8486        | 3.0   | 750  | 1.0263          | 0.3519   | 0.6632      | 0.6606   | 0.3642            |
| 0.7396        | 4.0   | 1000 | 1.0125          | 0.4195   | 0.7092      | 0.7192   | 0.4186            |
| 0.6425        | 5.0   | 1250 | 1.0983          | 0.3910   | 0.6746      | 0.6826   | 0.3925            |
| 0.5648        | 6.0   | 1500 | 1.0948          | 0.4184   | 0.7145      | 0.7222   | 0.4089            |
| 0.4858        | 7.0   | 1750 | 1.1658          | 0.4242   | 0.7058      | 0.7184   | 0.4279            |
| 0.4329        | 8.0   | 2000 | 1.3020          | 0.4178   | 0.6806      | 0.6849   | 0.4081            |
| 0.3799        | 9.0   | 2250 | 1.2622          | 0.4466   | 0.7004      | 0.7055   | 0.4419            |
| 0.326         | 10.0  | 2500 | 1.3822          | 0.4162   | 0.6971      | 0.7032   | 0.4048            |
| 0.2849        | 11.0  | 2750 | 1.4716          | 0.3933   | 0.6941      | 0.6971   | 0.3826            |
| 0.251         | 12.0  | 3000 | 1.5651          | 0.4259   | 0.6928      | 0.6956   | 0.4231            |
| 0.2205        | 13.0  | 3250 | 1.6920          | 0.4257   | 0.6942      | 0.7032   | 0.4112            |
| 0.205         | 14.0  | 3500 | 1.7016          | 0.4269   | 0.6899      | 0.6872   | 0.4260            |
| 0.1946        | 15.0  | 3750 | 1.7647          | 0.4312   | 0.6891      | 0.6910   | 0.4232            |
| 0.1661        | 16.0  | 4000 | 1.8255          | 0.4168   | 0.6886      | 0.6933   | 0.4003            |
| 0.1502        | 17.0  | 4250 | 1.8261          | 0.4190   | 0.6950      | 0.7040   | 0.3996            |
| 0.1625        | 18.0  | 4500 | 1.8163          | 0.4260   | 0.7001      | 0.7047   | 0.4079            |
| 0.1329        | 19.0  | 4750 | 1.8274          | 0.4368   | 0.7023      | 0.7055   | 0.4218            |
| 0.1248        | 20.0  | 5000 | 1.8388          | 0.4307   | 0.6983      | 0.7032   | 0.4139            |


### Framework versions

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