Instructions to use d4data/EnviDueDiligence_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d4data/EnviDueDiligence_NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="d4data/EnviDueDiligence_NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("d4data/EnviDueDiligence_NER") model = AutoModelForTokenClassification.from_pretrained("d4data/EnviDueDiligence_NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 190fd4ba3f58a657fac85d8a9dcf3cb2258e5ab10fdeac7ef488ad276aaf2bdc
- Size of remote file:
- 4.47 kB
- SHA256:
- 7055907b6d27e74601fff328ea1fadd2feac1546e616919fc601fadf9570622d
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