Instructions to use denizspynk/req_mod_ner_modelv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use denizspynk/req_mod_ner_modelv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="denizspynk/req_mod_ner_modelv2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("denizspynk/req_mod_ner_modelv2") model = AutoModelForTokenClassification.from_pretrained("denizspynk/req_mod_ner_modelv2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from denizspynk/req_mod_ner_modelv2: direct link, hf CLI and curl.
- Browser
- Download file 465 MB
-
https://huggingface.co/denizspynk/req_mod_ner_modelv2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://denizspynk/req_mod_ner_modelv2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/denizspynk/req_mod_ner_modelv2/resolve/main/pytorch_model.bin
465 MB
- Xet hash:
- c2d40fc92b3bad59daf9027c3127c2f5002f174fd52ac48f078597d9faa6540a
- Size of remote file:
- 465 MB
- SHA256:
- 30921c36bcd97a2fb12972384a4ad3d82ec46e9aae547ae106b435d25a383fc2
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