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---
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
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