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library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: emotion_roberta_weighted
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_roberta_weighted
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2084
- Accuracy: 0.9315
- Precision: 0.9379
- Recall: 0.9315
- F1: 0.9331
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3177 | 1.0 | 1000 | 0.2423 | 0.918 | 0.9245 | 0.918 | 0.9195 |
| 0.1804 | 2.0 | 2000 | 0.1776 | 0.931 | 0.9366 | 0.931 | 0.9317 |
| 0.1504 | 3.0 | 3000 | 0.1740 | 0.935 | 0.9410 | 0.935 | 0.9362 |
| 0.1203 | 4.0 | 4000 | 0.1723 | 0.9405 | 0.9438 | 0.9405 | 0.9413 |
| 0.0889 | 5.0 | 5000 | 0.2068 | 0.939 | 0.9420 | 0.939 | 0.9398 |
### Framework versions
- Transformers 4.57.2
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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