Instructions to use dcram/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dcram/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dcram/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dcram/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("dcram/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dcram/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/dcram/bert-finetuned-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://dcram/bert-finetuned-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dcram/bert-finetuned-ner/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 2ac63b0bf16ac0b7afa4324ee9d26aa68b0b9b305ab048a7f95477edf290a8a7
- Size of remote file:
- 4.92 kB
- SHA256:
- 22d23ad49bd730a62b7e2f853fa5d27870cb7986d2dcc7eb5a2641a402af66a0
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