Token Classification
Transformers
Safetensors
Indonesian
English
xlm-roberta
named-entity-recognition
indonesian
english
multilingual
social-media
Instructions to use nahiar/xlm-roberta-ner-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nahiar/xlm-roberta-ner-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nahiar/xlm-roberta-ner-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("nahiar/xlm-roberta-ner-v2") model = AutoModelForTokenClassification.from_pretrained("nahiar/xlm-roberta-ner-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| global_step,train_loss,eval_loss,precision,recall,f1_score | |
| 446,0.19103847444057465,0.12188560444001968,0.7763289869608826,0.8310070861069357,0.8027380211574362 | |
| 892,0.1230345368385315,0.12026308792141768,0.8048780487804879,0.836160618423878,0.8202211690363348 | |
| 1338,0.22532741725444794,0.11580085496489818,0.8149502335973999,0.8614988189821774,0.8375782881002087 | |
| 1784,0.010530168190598488,0.1234030959984431,0.8205499276410999,0.8522654069143225,0.8361070149568148 | |
| 2000,0.016664333641529083,0.131387118393412,0.8146631169363097,0.8541979815331758,0.8339622641509434 | |
| 2230,0.0292290598154068,0.12977997700755411,0.8170806815848901,0.8546274425595877,0.8354324097397144 | |