Instructions to use SlayerLab/NERGAL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SlayerLab/NERGAL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="SlayerLab/NERGAL")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("SlayerLab/NERGAL") model = AutoModelForTokenClassification.from_pretrained("SlayerLab/NERGAL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "full_name": "Named Entity Recognition with Grounded Additive Labels", | |
| "hub_id": "SlayerLab/NERGAL", | |
| "version": "1.0.2", | |
| "mode": "rules_union", | |
| "epoch": 5, | |
| "seed": 202609160, | |
| "threshold": 0.95, | |
| "rules_sha256": "3016ae5bd403ff997458f9dd74bad8c6ed1388eb83dadc1b31cdb182f9ed607f", | |
| "eval": { | |
| "split": "841-dev", | |
| "gold_entities": 354, | |
| "whole_entities": 324, | |
| "residual_passages": 24, | |
| "union_fp": 123, | |
| "rules_fp": 98, | |
| "character_precision": 0.9793, | |
| "character_recall": 0.9612, | |
| "exact_precision": 0.8634, | |
| "exact_recall": 0.8927, | |
| "exact_f1": 0.8778, | |
| "phone_whole": 145, | |
| "phone_gold": 169, | |
| "pii_whole": 179, | |
| "pii_gold": 185 | |
| }, | |
| "weights_sha256": "063ee5f9782c1b4d99e838be5a836328718e1372e213d5b87098b371b5c162af", | |
| "gaps": [ | |
| "[PII_SPACE]", | |
| "[PII_BREAK]" | |
| ], | |
| "gap_ids": [ | |
| 250002, | |
| 250003 | |
| ], | |
| "drop_in_token_classification_pipeline": false, | |
| "promotion_authorized": false, | |
| "backbone": { | |
| "repo": "FacebookAI/xlm-roberta-large", | |
| "revision": "c23d21b0620b635a76227c604d44e43a9f0ee389" | |
| } | |
| } | |