Token Classification
Transformers
PyTorch
Spanish
roberta
PyTorch
Transformers
Token Classification
roberta-base-bne
Instructions to use sdocio/es_trf_ner_cds_bne-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sdocio/es_trf_ner_cds_bne-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sdocio/es_trf_ner_cds_bne-base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sdocio/es_trf_ner_cds_bne-base") model = AutoModelForTokenClassification.from_pretrained("sdocio/es_trf_ner_cds_bne-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9983546594777942, | |
| "eval_f1": 0.9674450707465777, | |
| "eval_loss": 0.007044909987598658, | |
| "eval_precision": 0.9653351698806244, | |
| "eval_recall": 0.9695642148950888, | |
| "eval_runtime": 16.6647, | |
| "eval_samples": 15178, | |
| "eval_samples_per_second": 910.788, | |
| "eval_steps_per_second": 113.893, | |
| "train_loss": 0.009494402144574594, | |
| "train_runtime": 283.3545, | |
| "train_samples": 45533, | |
| "train_samples_per_second": 482.078, | |
| "train_steps_per_second": 15.066 | |
| } |