---
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: 1.6603
- Macro f1: 0.4329
- Weighted f1: 0.7053
- Accuracy: 0.7154
- Balanced accuracy: 0.4114

## 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: 3e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:|
| 1.3633        | 1.0   | 125  | 1.1325          | 0.3442   | 0.6470      | 0.6872   | 0.3862            |
| 1.0162        | 2.0   | 250  | 0.9858          | 0.3062   | 0.6889      | 0.7131   | 0.3135            |
| 0.868         | 3.0   | 375  | 0.9587          | 0.4091   | 0.7071      | 0.7207   | 0.3993            |
| 0.75          | 4.0   | 500  | 0.9983          | 0.4105   | 0.7080      | 0.7192   | 0.4039            |
| 0.6317        | 5.0   | 625  | 1.0197          | 0.4095   | 0.6941      | 0.6994   | 0.4093            |
| 0.5253        | 6.0   | 750  | 1.0760          | 0.4303   | 0.7073      | 0.7123   | 0.4223            |
| 0.4615        | 7.0   | 875  | 1.1371          | 0.4328   | 0.7040      | 0.7169   | 0.4096            |
| 0.3984        | 8.0   | 1000 | 1.1649          | 0.4516   | 0.6997      | 0.7002   | 0.4678            |
| 0.3332        | 9.0   | 1125 | 1.2009          | 0.4364   | 0.6994      | 0.7040   | 0.4243            |
| 0.2996        | 10.0  | 1250 | 1.2760          | 0.4336   | 0.7095      | 0.7192   | 0.4162            |
| 0.255         | 11.0  | 1375 | 1.3266          | 0.4353   | 0.6914      | 0.6918   | 0.4402            |
| 0.2318        | 12.0  | 1500 | 1.3591          | 0.4322   | 0.7011      | 0.7116   | 0.4101            |
| 0.2163        | 13.0  | 1625 | 1.4554          | 0.4226   | 0.7080      | 0.7237   | 0.4029            |
| 0.1837        | 14.0  | 1750 | 1.4363          | 0.4385   | 0.6938      | 0.6963   | 0.4250            |
| 0.1735        | 15.0  | 1875 | 1.5356          | 0.4363   | 0.7118      | 0.7230   | 0.4098            |
| 0.1526        | 16.0  | 2000 | 1.5731          | 0.4370   | 0.7073      | 0.7169   | 0.4181            |
| 0.1288        | 17.0  | 2125 | 1.6258          | 0.4406   | 0.7123      | 0.7245   | 0.4151            |
| 0.1321        | 18.0  | 2250 | 1.6590          | 0.4364   | 0.7081      | 0.7184   | 0.4148            |
| 0.114         | 19.0  | 2375 | 1.6598          | 0.4324   | 0.7074      | 0.7192   | 0.4081            |
| 0.1063        | 20.0  | 2500 | 1.6603          | 0.4329   | 0.7053      | 0.7154   | 0.4114            |


### Framework versions

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