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 training_args.bin from denizspynk/req_mod_ner_modelv2: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/denizspynk/req_mod_ner_modelv2/resolve/main/training_args.bin
- Command line
-
hf download hf://denizspynk/req_mod_ner_modelv2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/denizspynk/req_mod_ner_modelv2/resolve/main/training_args.bin
3.39 kB
- Xet hash:
- 0a4d8a7fa746f4e0ad7a4fc9febda57f1c355e5d7944875c2c7bcd6f915134f4
- Size of remote file:
- 3.39 kB
- SHA256:
- 584107cecca251c5ca45eb9a35fb46e3ad1588b4d38632c1b65449444ed89ecd
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