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Comentarios_AgresivosNoAgresivos
weicap/Comentarios_AgresivosNoAgresivos
Comentarios_AgresivosNoAgresivos is a text classification model from weicap. 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
- roberta
- Access
- Public
- Created
- Oct 24, 2022
- Updated
- Oct 24, 2022
- SHA
- d38129a0
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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. -->
Comentarios_AgresivosNoAgresivos
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4584
- Accuracy: 0.8162
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6215 | 1.0 | 154 | 0.5717 | 0.7299 |
| 0.5075 | 2.0 | 308 | 0.4193 | 0.8248 |
| 0.2436 | 3.0 | 462 | 0.4037 | 0.8540 |
| 0.0571 | 4.0 | 616 | 0.6594 | 0.8467 |
| 0.0242 | 5.0 | 770 | 1.0059 | 0.8029 |
| 0.0497 | 6.0 | 924 | 0.8195 | 0.8394 |
| 0.0005 | 7.0 | 1078 | 0.9234 | 0.8394 |
| 0.0528 | 8.0 | 1232 | 0.8894 | 0.8394 |
| 0.0003 | 9.0 | 1386 | 0.9285 | 0.8321 |
| 0.0003 | 10.0 | 1540 | 0.9749 | 0.8321 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Oct 24, 2022
- Updated
- Oct 24, 2022