Instructions to use userABC123/super_cnll_2e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use userABC123/super_cnll_2e with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="userABC123/super_cnll_2e")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("userABC123/super_cnll_2e") model = AutoModelForTokenClassification.from_pretrained("userABC123/super_cnll_2e", device_map="auto") - Notebooks
- Google Colab
- Kaggle
super_cnll_2e
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1780
- Precision: 0.5906
- Recall: 0.6667
- F1: 0.6264
- Accuracy: 0.9446
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: 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 63 | 0.2281 | 0.5242 | 0.5710 | 0.5466 | 0.9322 |
| No log | 2.0 | 126 | 0.1780 | 0.5906 | 0.6667 | 0.6264 | 0.9446 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cpu
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for userABC123/super_cnll_2e
Base model
dccuchile/bert-base-spanish-wwm-cased