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my-model-SaBERT-Sentimento

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lBober/my-model-Sabert-Sentimento

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  1. README.md +13 -13
  2. model.safetensors +1 -1
  3. runs/May25_16-02-41_fe998d142f54/events.out.tfevents.1716652962.fe998d142f54.15507.0 +3 -0
  4. runs/May25_16-05-16_fe998d142f54/events.out.tfevents.1716653117.fe998d142f54.16339.0 +3 -0
  5. runs/May25_16-08-12_fe998d142f54/events.out.tfevents.1716653293.fe998d142f54.17264.0 +3 -0
  6. runs/May25_16-11-21_fe998d142f54/events.out.tfevents.1716653482.fe998d142f54.18225.0 +3 -0
  7. training_args.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [VerificadoProfesional/SaBERT-Spanish-Sentiment-Analysis](https://huggingface.co/VerificadoProfesional/SaBERT-Spanish-Sentiment-Analysis) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2475
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- - Accuracy: 0.9355
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- - F1: 0.9244
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- - Precision: 0.9204
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- - Recall: 0.9355
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  ## Model description
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@@ -43,9 +43,9 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 2e-06
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- - train_batch_size: 20
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- - eval_batch_size: 20
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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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | 0.8062 | 1.0 | 37 | 0.5185 | 0.9419 | 0.9248 | 0.9389 | 0.9419 |
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- | 0.4799 | 2.0 | 74 | 0.3278 | 0.9419 | 0.9248 | 0.9389 | 0.9419 |
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- | 0.3556 | 3.0 | 111 | 0.2730 | 0.9355 | 0.9244 | 0.9204 | 0.9355 |
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- | 0.3058 | 4.0 | 148 | 0.2532 | 0.9355 | 0.9244 | 0.9204 | 0.9355 |
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- | 0.2785 | 5.0 | 185 | 0.2475 | 0.9355 | 0.9244 | 0.9204 | 0.9355 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [VerificadoProfesional/SaBERT-Spanish-Sentiment-Analysis](https://huggingface.co/VerificadoProfesional/SaBERT-Spanish-Sentiment-Analysis) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4177
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+ - Accuracy: 0.8710
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+ - F1: 0.8109
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+ - Precision: 0.7586
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+ - Recall: 0.8710
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.002
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+ - train_batch_size: 5
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+ - eval_batch_size: 5
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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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 0.7015 | 1.0 | 145 | 0.3931 | 0.8710 | 0.8109 | 0.7586 | 0.8710 |
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+ | 0.5409 | 2.0 | 290 | 0.6716 | 0.8710 | 0.8109 | 0.7586 | 0.8710 |
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+ | 0.4978 | 3.0 | 435 | 0.7986 | 0.8710 | 0.8109 | 0.7586 | 0.8710 |
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+ | 0.5863 | 4.0 | 580 | 0.3856 | 0.8710 | 0.8109 | 0.7586 | 0.8710 |
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+ | 0.4274 | 5.0 | 725 | 0.4177 | 0.8710 | 0.8109 | 0.7586 | 0.8710 |
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  ### Framework versions
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