Token Classification
SpanMarker
PyTorch
Spanish
ner
named-entity-recognition
generated_from_span_marker_trainer
Eval Results (legacy)
Instructions to use alvarobartt/span-marker-xlm-roberta-large-conll-2002-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SpanMarker
How to use alvarobartt/span-marker-xlm-roberta-large-conll-2002-es with SpanMarker:
from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("alvarobartt/span-marker-xlm-roberta-large-conll-2002-es") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 2.0, | |
| "eval_LOC": { | |
| "f1": 0.8797927461139897, | |
| "number": 985, | |
| "precision": 0.8984126984126984, | |
| "recall": 0.8619289340101522 | |
| }, | |
| "eval_MISC": { | |
| "f1": 0.6847545219638242, | |
| "number": 436, | |
| "precision": 0.7840236686390533, | |
| "recall": 0.6077981651376146 | |
| }, | |
| "eval_ORG": { | |
| "f1": 0.8912097476066145, | |
| "number": 1685, | |
| "precision": 0.8717366628830874, | |
| "recall": 0.9115727002967359 | |
| }, | |
| "eval_PER": { | |
| "f1": 0.9655455291222313, | |
| "number": 1222, | |
| "precision": 0.9679276315789473, | |
| "recall": 0.9631751227495908 | |
| }, | |
| "eval_loss": 0.00711033632978797, | |
| "eval_overall_accuracy": 0.9799255240723589, | |
| "eval_overall_f1": 0.8911398300151355, | |
| "eval_overall_precision": 0.8981459751232105, | |
| "eval_overall_recall": 0.8842421441774492, | |
| "eval_runtime": 21.7881, | |
| "eval_samples_per_second": 132.549, | |
| "eval_steps_per_second": 16.569 | |
| } |