Instructions to use codingJacob/distilbert-base-uncased-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codingJacob/distilbert-base-uncased-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="codingJacob/distilbert-base-uncased-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("codingJacob/distilbert-base-uncased-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("codingJacob/distilbert-base-uncased-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Align label mapping with conll2003 dataset
Hi there, your model is using a default label mapping. Accept this PR to align the label mapping with the `conll2003` dataset this model was trained on. This will enable your model to be evaluated by [Hugging Face's automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=conll2003)6001f25
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add model
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initial commit
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