Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use sachin2000keshav/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sachin2000keshav/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sachin2000keshav/bert-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sachin2000keshav/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("sachin2000keshav/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sachin2000keshav/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/sachin2000keshav/bert-finetuned-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sachin2000keshav/bert-finetuned-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sachin2000keshav/bert-finetuned-ner/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 36cb1dbdd3afa458c80e67db5a39022c23cb073a044799abf7e7687172ecf249
- Size of remote file:
- 431 MB
- SHA256:
- 88c7b86469f67da1e62a27235ae548af4bc9daf24c0d919eca16b954c0b59cb4
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