Instructions to use THemidli/applied-ner-stage3-bert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use THemidli/applied-ner-stage3-bert-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="THemidli/applied-ner-stage3-bert-tiny")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("THemidli/applied-ner-stage3-bert-tiny") model = AutoModelForTokenClassification.from_pretrained("THemidli/applied-ner-stage3-bert-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,375 Bytes
205eeb6 | 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": 0.42148148148148146,
"overall_recall": 0.5273401297497683,
"overall_f1": 0.4685055578427336,
"overall_accuracy": 0.8269166759125708,
"person_precision": 0.4612546125461255,
"person_recall": 0.6410256410256411,
"person_f1_score": 0.5364806866952789,
"person_support": 195.0,
"organization_precision": 0.2,
"organization_recall": 0.21768707482993196,
"organization_f1_score": 0.20846905537459287,
"organization_support": 147.0,
"location_precision": 0.43169398907103823,
"location_recall": 0.5524475524475524,
"location_f1_score": 0.48466257668711654,
"location_support": 143.0,
"timedate_precision": 0.7921348314606742,
"timedate_recall": 0.844311377245509,
"timedate_f1_score": 0.817391304347826,
"timedate_support": 167.0,
"product_precision": 0.17142857142857143,
"product_recall": 0.1889763779527559,
"product_f1_score": 0.1797752808988764,
"product_support": 127.0,
"workofart_precision": 0.1276595744680851,
"workofart_recall": 0.24742268041237114,
"workofart_f1_score": 0.16842105263157897,
"workofart_support": 97.0,
"job_precision": 0.6991150442477876,
"job_recall": 0.797979797979798,
"job_f1_score": 0.7452830188679246,
"job_support": 99.0,
"amount_precision": 0.5555555555555556,
"amount_recall": 0.625,
"amount_f1_score": 0.5882352941176471,
"amount_support": 104.0
}
|