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# Misinformation-Covid-xlm-roberta-base

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: 0.7194
- F1: 0.4333

## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6737        | 1.0   | 189  | 0.6662          | 0.0    |
| 0.7083        | 2.0   | 378  | 0.6540          | 0.0    |
| 0.7185        | 3.0   | 567  | 0.8346          | 0.0    |
| 0.7826        | 4.0   | 756  | 0.8685          | 0.0    |
| 0.8333        | 5.0   | 945  | 0.7939          | 0.0    |
| 0.7989        | 6.0   | 1134 | 0.8978          | 0.0    |
| 0.8009        | 7.0   | 1323 | 0.7276          | 0.3265 |
| 0.6824        | 8.0   | 1512 | 0.7733          | 0.3774 |
| 0.6979        | 9.0   | 1701 | 0.7327          | 0.4407 |
| 0.6963        | 10.0  | 1890 | 0.7194          | 0.4333 |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.2
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "Misinformation-Covid-xlm-roberta-base", "results": []}]}
transformers pytorch xlm-roberta text-classification generated_from_trainer base_model:xlm-roberta-base license:mit autotrain_compatible endpoints_compatible region:us