Instructions to use jndvs/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jndvs/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jndvs/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jndvs/results") model = AutoModelForSequenceClassification.from_pretrained("jndvs/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
results
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0980
- F1: 0.9751
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 60 | 0.0980 | 0.9751 |
| No log | 2.0 | 120 | 0.0945 | 0.9751 |
| No log | 3.0 | 180 | 0.0874 | 0.9751 |
| No log | 4.0 | 240 | 0.0748 | 0.9751 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cpu
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for jndvs/results
Base model
dccuchile/bert-base-spanish-wwm-cased