Instructions to use Shaileshmnnit/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shaileshmnnit/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Shaileshmnnit/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Shaileshmnnit/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("Shaileshmnnit/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Shaileshmnnit/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/Shaileshmnnit/bert-finetuned-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://Shaileshmnnit/bert-finetuned-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Shaileshmnnit/bert-finetuned-ner/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 5d1913579ffd36c0e0a66c1a9e43115b53f87e3e9d9fb8529c7fefcd5444e09e
- Size of remote file:
- 3.58 kB
- SHA256:
- 2bf54a75dfb2eb1b81061a8b778551b1265566058926f2bdadfb07c5e912c3e8
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