Instructions to use thetkrans/ft_checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thetkrans/ft_checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="thetkrans/ft_checkpoints")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("thetkrans/ft_checkpoints") model = AutoModelForSequenceClassification.from_pretrained("thetkrans/ft_checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ft_checkpoints
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: 1.5479
- Accuracy: 0.4615
- F1: 0.4354
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: 8
- eval_batch_size: 16
- 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: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.4168 | 1.0 | 26 | 1.2937 | 0.5192 | 0.4251 |
| 1.0537 | 2.0 | 52 | 1.3103 | 0.3846 | 0.2389 |
| 0.8208 | 3.0 | 78 | 1.4127 | 0.4231 | 0.2685 |
| 0.7214 | 4.0 | 104 | 1.4151 | 0.4231 | 0.3658 |
| 0.5100 | 5.0 | 130 | 1.5376 | 0.4231 | 0.4015 |
| 0.3777 | 6.0 | 156 | 1.5479 | 0.4615 | 0.4354 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Downloads last month
- 6
Model tree for thetkrans/ft_checkpoints
Base model
dccuchile/bert-base-spanish-wwm-cased