Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use MelisaO/modelo_clasificacion_violencia7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MelisaO/modelo_clasificacion_violencia7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MelisaO/modelo_clasificacion_violencia7")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MelisaO/modelo_clasificacion_violencia7") model = AutoModelForSequenceClassification.from_pretrained("MelisaO/modelo_clasificacion_violencia7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
modelo_clasificacion_violencia7
This model is a fine-tuned version of MelisaO/modelo_clasificacion_violencia5 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 90 | 0.0818 |
| 0.0166 | 2.0 | 180 | 0.0000 |
| 0.0 | 3.0 | 270 | 0.0000 |
| 0.0 | 4.0 | 360 | 0.0000 |
| 0.0 | 5.0 | 450 | 0.0000 |
| 0.0 | 6.0 | 540 | 0.0000 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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