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
File size: 1,200 Bytes
caa10a8 78cc85a caa10a8 2bf5c25 7ce967b 15eb2b4 7ce967b 15eb2b4 7ce967b 2bf5c25 caa10a8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | {
"full_name": "Named Entity Recognition with Grounded Additive Labels",
"hub_id": "SlayerLab/NERGAL",
"version": "1.1.0",
"mode": "rules_union",
"epoch": 5,
"seed": 202609160,
"threshold": 0.95,
"rules_sha256": "f32d5c5452fc47178e109d4bc248a0d8234ea6e59e8cf79407f4eb8451581d67",
"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
},
"source_checkpoint_sha256": "063ee5f9782c1b4d99e838be5a836328718e1372e213d5b87098b371b5c162af",
"model_safetensors_sha256": "1d42c34459e90cd25db44f92bb31fb2be1ef3a1b1e34c599162e1895057c08f6",
"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"
}
}
|