---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
base_model: xlm-roberta-base
model-index:
- name: sentiment-10Epochs-2-work-please
  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. -->

# sentiment-10Epochs-2-work-please

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7450
- Accuracy: 0.8549
- F1: 0.8516
- Precision: 0.8714
- Recall: 0.8327

## 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: 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 | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3685        | 1.0   | 7088  | 0.4334          | 0.8590   | 0.8463 | 0.9304    | 0.7762 |
| 0.3721        | 2.0   | 14176 | 0.3822          | 0.8673   | 0.8575 | 0.9257    | 0.7987 |
| 0.3393        | 3.0   | 21264 | 0.4634          | 0.8705   | 0.8619 | 0.9228    | 0.8086 |
| 0.3017        | 4.0   | 28352 | 0.4806          | 0.8708   | 0.8630 | 0.9186    | 0.8137 |
| 0.3072        | 5.0   | 35440 | 0.4509          | 0.87     | 0.8648 | 0.9009    | 0.8314 |
| 0.2833        | 6.0   | 42528 | 0.5339          | 0.8627   | 0.8581 | 0.8879    | 0.8302 |
| 0.2633        | 7.0   | 49616 | 0.5457          | 0.8637   | 0.8614 | 0.8759    | 0.8473 |
| 0.2418        | 8.0   | 56704 | 0.6408          | 0.8589   | 0.8563 | 0.8722    | 0.8410 |
| 0.1999        | 9.0   | 63792 | 0.7404          | 0.8530   | 0.8485 | 0.8752    | 0.8235 |
| 0.1809        | 10.0  | 70880 | 0.7450          | 0.8549   | 0.8516 | 0.8714    | 0.8327 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0
- Datasets 2.0.0
- Tokenizers 0.11.6