---
library_name: transformers
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: emotion_model
  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. -->

# emotion_model

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1691
- Macro F1: 0.5721
- Micro F1: 0.7014
- Accuracy: 0.8780

## 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: 1.5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 0.2428        | 1.0   | 143  | 0.2269          | 0.0016   | 0.0028   | 0.7811   |
| 0.1979        | 2.0   | 286  | 0.1774          | 0.4377   | 0.6399   | 0.8642   |
| 0.1712        | 3.0   | 429  | 0.1669          | 0.4939   | 0.6727   | 0.8729   |
| 0.1571        | 4.0   | 572  | 0.1635          | 0.5474   | 0.6889   | 0.8768   |
| 0.1426        | 5.0   | 715  | 0.1666          | 0.5658   | 0.6881   | 0.8737   |
| 0.1335        | 6.0   | 858  | 0.1665          | 0.5824   | 0.6999   | 0.8750   |
| 0.1236        | 7.0   | 1001 | 0.1682          | 0.5765   | 0.6940   | 0.8735   |
| 0.1152        | 8.0   | 1144 | 0.1697          | 0.5747   | 0.6964   | 0.8752   |
| 0.1104        | 9.0   | 1287 | 0.1732          | 0.5708   | 0.6930   | 0.8732   |
| 0.1069        | 10.0  | 1430 | 0.1742          | 0.5814   | 0.6959   | 0.8738   |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.3.1.post300
- Datasets 2.2.1
- Tokenizers 0.21.0