pytorch xlm-roberta text-classification transformers generated_from_trainer license:mit model-index
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-cv-studio_name-pooler
  results: []
---

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# xlm-roberta-base-cv-studio_name-pooler

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1268
- Accuracy: 0.6758
- F1 Micro: 0.6758
- F1 Macro: 0.3836
- Precision Micro: 0.6758
- Recall Micro: 0.6758

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Micro | F1 Macro | Precision Micro | Recall Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|:------------:|
| 2.0159        | 1.59  | 1000 | 1.6867          | 0.4801   | 0.4801   | 0.1389   | 0.4801          | 0.4801       |
| 1.5921        | 3.18  | 2000 | 1.4606          | 0.5342   | 0.5342   | 0.2238   | 0.5342          | 0.5342       |
| 1.447         | 4.77  | 3000 | 1.3170          | 0.6114   | 0.6114   | 0.3021   | 0.6114          | 0.6114       |
| 1.3441        | 6.36  | 4000 | 1.2777          | 0.6181   | 0.6181   | 0.3264   | 0.6181          | 0.6181       |
| 1.2847        | 7.95  | 5000 | 1.1902          | 0.6555   | 0.6555   | 0.3495   | 0.6555          | 0.6555       |
| 1.2296        | 9.54  | 6000 | 1.1867          | 0.6635   | 0.6635   | 0.3608   | 0.6635          | 0.6635       |
| 1.1965        | 11.13 | 7000 | 1.1426          | 0.6683   | 0.6683   | 0.3728   | 0.6683          | 0.6683       |
| 1.1547        | 12.72 | 8000 | 1.1419          | 0.6687   | 0.6687   | 0.3743   | 0.6687          | 0.6687       |
| 1.1677        | 14.31 | 9000 | 1.1268          | 0.6758   | 0.6758   | 0.3836   | 0.6758          | 0.6758       |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.8.1+cu111
- Datasets 1.6.2
- Tokenizers 0.12.1
