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--- |
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base_model: daveni/twitter-xlm-roberta-emotion-es |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: xml-roberta-HU-Com |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# xml-roberta-HU-Com |
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This model is a fine-tuned version of [daveni/twitter-xlm-roberta-emotion-es](https://huggingface.co./daveni/twitter-xlm-roberta-emotion-es) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3693 |
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- Accuracy: 0.7911 |
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- F1: 0.7440 |
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- Precision: 0.7415 |
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- Recall: 0.7466 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.6717 | 1.0 | 90 | 0.5918 | 0.6852 | 0.5272 | 0.6774 | 0.4315 | |
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| 0.453 | 2.0 | 180 | 0.5358 | 0.7465 | 0.6403 | 0.7570 | 0.5548 | |
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| 0.2631 | 3.0 | 270 | 0.7088 | 0.7744 | 0.7273 | 0.7152 | 0.7397 | |
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| 0.1936 | 4.0 | 360 | 0.7078 | 0.7939 | 0.7566 | 0.7278 | 0.7877 | |
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| 0.1273 | 5.0 | 450 | 1.1057 | 0.7772 | 0.7436 | 0.6988 | 0.7945 | |
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| 0.066 | 6.0 | 540 | 1.1990 | 0.7799 | 0.7168 | 0.7519 | 0.6849 | |
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| 0.0286 | 7.0 | 630 | 1.2457 | 0.7994 | 0.7584 | 0.7434 | 0.7740 | |
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| 0.0261 | 8.0 | 720 | 1.3297 | 0.7799 | 0.7106 | 0.7638 | 0.6644 | |
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| 0.0097 | 9.0 | 810 | 1.3733 | 0.7855 | 0.7354 | 0.7379 | 0.7329 | |
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| 0.0071 | 10.0 | 900 | 1.3693 | 0.7911 | 0.7440 | 0.7415 | 0.7466 | |
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### Framework versions |
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- Transformers 4.43.0.dev0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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