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my-model-MiniLM-Sentimento

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lBober/my-model-MiniLM-Sentimento

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  1. README.md +18 -17
  2. model.safetensors +1 -1
  3. runs/May26_09-41-24_6b3393198f09/events.out.tfevents.1716716484.6b3393198f09.11602.0 +3 -0
  4. runs/May26_09-41-24_6b3393198f09/events.out.tfevents.1716716571.6b3393198f09.11602.1 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -5,6 +5,9 @@ tags:
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  - generated_from_trainer
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  metrics:
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  - accuracy
 
 
 
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  model-index:
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  - name: my-model-MiniLM-Sentimento
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  results: []
@@ -17,8 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [Microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/Microsoft/Multilingual-MiniLM-L12-H384) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8509
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  - Accuracy: 0.8839
 
 
 
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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: 0.2
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- - train_batch_size: 10
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- - eval_batch_size: 10
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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 |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 27.7738 | 1.0 | 73 | 20.6452 | 0.1161 |
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- | 5.8834 | 2.0 | 146 | 5.1288 | 0.8839 |
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- | 6.7214 | 3.0 | 219 | 0.5695 | 0.8839 |
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- | 4.6995 | 4.0 | 292 | 1.5581 | 0.8839 |
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- | 7.1315 | 5.0 | 365 | 1.7513 | 0.8839 |
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- | 3.6237 | 6.0 | 438 | 1.4431 | 0.8839 |
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- | 2.8689 | 7.0 | 511 | 0.5693 | 0.8839 |
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- | 2.7399 | 8.0 | 584 | 1.2978 | 0.1161 |
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- | 2.279 | 9.0 | 657 | 1.1006 | 0.8839 |
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- | 1.3825 | 10.0 | 730 | 0.8509 | 0.8839 |
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  ### Framework versions
 
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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: my-model-MiniLM-Sentimento
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  results: []
 
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  This model is a fine-tuned version of [Microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/Microsoft/Multilingual-MiniLM-L12-H384) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3036
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  - Accuracy: 0.8839
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+ - F1: 0.8649
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+ - Precision: 0.8661
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+ - Recall: 0.8839
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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: 5e-05
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+ - train_batch_size: 8
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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: 5
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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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+ | 0.5534 | 1.0 | 91 | 0.4123 | 0.8645 | 0.8209 | 0.8914 | 0.8645 |
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+ | 0.3774 | 2.0 | 182 | 0.3823 | 0.8516 | 0.7834 | 0.7252 | 0.8516 |
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+ | 0.35 | 3.0 | 273 | 0.3656 | 0.8710 | 0.8257 | 0.8815 | 0.8710 |
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+ | 0.3119 | 4.0 | 364 | 0.3430 | 0.8903 | 0.8609 | 0.8964 | 0.8903 |
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+ | 0.2967 | 5.0 | 455 | 0.3036 | 0.8839 | 0.8649 | 0.8661 | 0.8839 |
 
 
 
 
 
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  ### Framework versions
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