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")# pip install -U transformers accelerate # 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
Download optimizer.pt from d4data/EnviDueDiligence_NER: direct link, hf CLI and curl.
- Browser
- Download file 531 MB
-
https://huggingface.co/d4data/EnviDueDiligence_NER/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://d4data/EnviDueDiligence_NER@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/d4data/EnviDueDiligence_NER/resolve/refs%2Fpr%2F1/optimizer.pt
531 MB
- Xet hash:
- 0eac64eace8b4f8156dcd402baba1708037f9e131b076f6e81d01e79c58520a8
- Size of remote file:
- 531 MB
- SHA256:
- 5f024224d860ad973809d56a2a2858ffbaa200be625bb8405d34a3458797ca77
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