---
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: 2.3471
- Macro f1: 0.4351
- Weighted f1: 0.7056
- Accuracy: 0.7207
- Balanced accuracy: 0.4063

## 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: 5e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:|
| 1.249         | 1.0   | 250  | 1.1782          | 0.2143   | 0.5844      | 0.6385   | 0.2417            |
| 1.0481        | 2.0   | 500  | 1.0009          | 0.3079   | 0.6757      | 0.6865   | 0.3192            |
| 0.903         | 3.0   | 750  | 1.0094          | 0.3105   | 0.6840      | 0.6986   | 0.3179            |
| 0.7604        | 4.0   | 1000 | 1.0636          | 0.3817   | 0.6834      | 0.6994   | 0.3751            |
| 0.6367        | 5.0   | 1250 | 1.0813          | 0.3999   | 0.6963      | 0.7108   | 0.3945            |
| 0.5293        | 6.0   | 1500 | 1.1597          | 0.3909   | 0.6920      | 0.6986   | 0.3895            |
| 0.4097        | 7.0   | 1750 | 1.3520          | 0.3517   | 0.6739      | 0.6865   | 0.3757            |
| 0.3442        | 8.0   | 2000 | 1.5343          | 0.4012   | 0.6684      | 0.6743   | 0.4028            |
| 0.2663        | 9.0   | 2250 | 1.5623          | 0.4241   | 0.7007      | 0.7154   | 0.4052            |
| 0.2383        | 10.0  | 2500 | 1.6971          | 0.4327   | 0.7080      | 0.7169   | 0.4179            |
| 0.2053        | 11.0  | 2750 | 1.7675          | 0.4331   | 0.7073      | 0.7177   | 0.4199            |
| 0.1698        | 12.0  | 3000 | 1.8678          | 0.4381   | 0.7103      | 0.7298   | 0.4097            |
| 0.1467        | 13.0  | 3250 | 2.0007          | 0.4343   | 0.7113      | 0.7268   | 0.4082            |
| 0.1098        | 14.0  | 3500 | 2.0797          | 0.4267   | 0.7004      | 0.7131   | 0.3986            |
| 0.1049        | 15.0  | 3750 | 2.2048          | 0.4190   | 0.7037      | 0.7192   | 0.3939            |
| 0.0912        | 16.0  | 4000 | 2.2582          | 0.4263   | 0.6903      | 0.7024   | 0.4003            |
| 0.0678        | 17.0  | 4250 | 2.2735          | 0.4276   | 0.7052      | 0.7222   | 0.4019            |
| 0.0623        | 18.0  | 4500 | 2.3478          | 0.4317   | 0.7048      | 0.7207   | 0.4030            |
| 0.0546        | 19.0  | 4750 | 2.3598          | 0.4298   | 0.7043      | 0.7207   | 0.4003            |
| 0.0415        | 20.0  | 5000 | 2.3471          | 0.4351   | 0.7056      | 0.7207   | 0.4063            |


### Framework versions

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