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Analisis-sentimientos-BETO-TASS-C
raulgdp/Analisis-sentimientos-BETO-TASS-C
Analisis-sentimientos-BETO-TASS-C is a text classification model from raulgdp. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Apr 21, 2024
- Updated
- Apr 22, 2024
- SHA
- 928e5f4d
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Base models
Model card
From the Hugging Face model README
<!-- 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. -->
Analisis-sentimientos-BETO-TASS-C
This model is a fine-tuned version of finiteautomata/beto-sentiment-analysis on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.2393
- Rmse: 0.7242
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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse |
|---|---|---|---|---|
| 0.8967 | 1.0 | 961 | 0.9813 | 0.6986 |
| 0.7239 | 2.0 | 1922 | 1.0296 | 0.7517 |
| 0.5218 | 3.0 | 2883 | 1.7505 | 0.7668 |
| 0.3103 | 4.0 | 3844 | 2.0306 | 0.7242 |
| 0.1974 | 5.0 | 4805 | 2.5881 | 0.7475 |
| 0.0896 | 6.0 | 5766 | 2.7333 | 0.7551 |
| 0.0444 | 7.0 | 6727 | 3.0139 | 0.7531 |
| 0.0255 | 8.0 | 7688 | 3.1289 | 0.7177 |
| 0.0137 | 9.0 | 8649 | 3.2251 | 0.7242 |
| 0.0069 | 10.0 | 9610 | 3.2393 | 0.7242 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Apr 21, 2024
- Updated
- Apr 22, 2024