Instructions to use THemidli/applied-ner-stage4-bert-tiny-improved with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use THemidli/applied-ner-stage4-bert-tiny-improved with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="THemidli/applied-ner-stage4-bert-tiny-improved")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("THemidli/applied-ner-stage4-bert-tiny-improved") model = AutoModelForTokenClassification.from_pretrained("THemidli/applied-ner-stage4-bert-tiny-improved", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,384 Bytes
48791c6 | 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 | {
"overall_precision": 0.9539951573849879,
"overall_recall": 0.9708781362007168,
"overall_f1": 0.9623626068613301,
"overall_accuracy": 0.9948478767829058,
"person_precision": 0.9862857142857143,
"person_recall": 0.9953863898500577,
"person_f1_score": 0.9908151549942594,
"person_support": 867.0,
"organization_precision": 0.9662162162162162,
"organization_recall": 0.9711375212224108,
"organization_f1_score": 0.9686706181202371,
"organization_support": 589.0,
"location_precision": 0.9738562091503268,
"location_recall": 0.9834983498349835,
"location_f1_score": 0.9786535303776683,
"location_support": 606.0,
"timedate_precision": 0.978978978978979,
"timedate_recall": 0.9834087481146304,
"timedate_f1_score": 0.981188863807374,
"timedate_support": 663.0,
"product_precision": 0.9625984251968503,
"product_recall": 0.9607072691552063,
"product_f1_score": 0.9616519174041297,
"product_support": 509.0,
"workofart_precision": 0.9707207207207207,
"workofart_recall": 0.9729119638826185,
"workofart_f1_score": 0.971815107102593,
"workofart_support": 443.0,
"job_precision": 0.7661691542288557,
"job_recall": 0.875,
"job_f1_score": 0.8169761273209549,
"job_support": 352.0,
"amount_precision": 0.9527027027027027,
"amount_recall": 0.9724137931034482,
"amount_f1_score": 0.9624573378839592,
"amount_support": 435.0
}
|