Instructions to use AGuevara/modelo_sentimiento_peruano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AGuevara/modelo_sentimiento_peruano with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AGuevara/modelo_sentimiento_peruano")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AGuevara/modelo_sentimiento_peruano") model = AutoModelForSequenceClassification.from_pretrained("AGuevara/modelo_sentimiento_peruano", device_map="auto") - Notebooks
- Google Colab
- Kaggle
modelo_sentimiento_peruano
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8422
- Accuracy: 0.6869
- F1: 0.6848
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: 8
- eval_batch_size: 8
- 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
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.6724 | 1.0 | 1167 | 0.7171 | 0.6981 | 0.6802 |
| 0.4647 | 2.0 | 2334 | 0.7981 | 0.6936 | 0.6939 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for AGuevara/modelo_sentimiento_peruano
Base model
dccuchile/bert-base-spanish-wwm-uncased