Token Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
Instructions to use harsh13333/ner_bert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harsh13333/ner_bert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="harsh13333/ner_bert_model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("harsh13333/ner_bert_model") model = AutoModelForTokenClassification.from_pretrained("harsh13333/ner_bert_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4457faecb429cca08c2b0214e360685967e91f151ee6455d082c2bbdd87da9f0
- Size of remote file:
- 669 kB
- SHA256:
- cb26b43c98e8266ae3e99c2a583cf8315d73b33a17e6b20b4df7ff1f22392d34
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