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---
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
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