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xlnet-tass-sentimientos
YoliGuadir/xlnet-tass-sentimientos
xlnet-tass-sentimientos is a text classification model from YoliGuadir. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlnet
- Access
- Public
- Created
- Jun 27, 2026
- Updated
- Jun 29, 2026
- SHA
- 99c94c66
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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. -->
xlnet-tass-sentimientos
This model is a fine-tuned version of xlnet-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1651
- F1 Macro: 0.5359
- F1 Weighted: 0.5381
- Accuracy: 0.5437
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: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Weighted | Accuracy |
|---|---|---|---|---|---|---|
| 1.0995 | 1.0 | 256 | 1.0782 | 0.3230 | 0.3319 | 0.4036 |
| 1.0417 | 2.0 | 512 | 0.9703 | 0.5142 | 0.5198 | 0.5451 |
| 0.9544 | 3.0 | 768 | 1.0096 | 0.4826 | 0.4931 | 0.5270 |
| 0.7986 | 4.0 | 1024 | 1.0544 | 0.5177 | 0.5216 | 0.5326 |
| 0.6990 | 5.0 | 1280 | 1.0772 | 0.5387 | 0.5429 | 0.5534 |
| 0.6277 | 6.0 | 1536 | 1.1651 | 0.5359 | 0.5381 | 0.5437 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cpu
- Datasets 4.8.5
- Tokenizers 0.22.2
Metricas de desempeno — 3 configuraciones de batch size
| Batch | Epocas | F1-Macro | F1-Weighted | Accuracy | Supera 64% | Mejor |
|---|---|---|---|---|---|---|
| 8 | 6 | 0.5339 | 0.54 | 0.55 | NO | |
| 16 | 6 | 0.5339 | 0.54 | 0.55 | NO | <- MEJOR |
| 32 | 6 | 0.5277 | 0.53 | 0.54 | NO |
El modelo publicado corresponde al batch=16 con F1-Macro=0.5339. XLNet no supero el baseline de BETO (64%) por limitaciones de hardware (float32 obligatorio, sin soporte fp16).
- Task
- Text Classification
- Library
- transformers
- Type
- xlnet
- License
- mit
- Created
- Jun 27, 2026
- Updated
- Jun 29, 2026