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:
- 2e5df77c7b18471fd6ee5408762e6a74d442016bb88da14852122787b56c4303
- Size of remote file:
- 266 MB
- SHA256:
- 8328807e1aa2b39f534edc43a7f73503046ecaf901f49f06a477ac1519cbb170
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