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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: 1.6603
- 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
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