Instructions to use DeepaPeri/MBERT-TRAINED-CONLL2003-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepaPeri/MBERT-TRAINED-CONLL2003-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="DeepaPeri/MBERT-TRAINED-CONLL2003-en")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("DeepaPeri/MBERT-TRAINED-CONLL2003-en") model = AutoModelForTokenClassification.from_pretrained("DeepaPeri/MBERT-TRAINED-CONLL2003-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from DeepaPeri/MBERT-TRAINED-CONLL2003-en: direct link, hf CLI and curl.
- Browser
- Download file 539 MB
-
https://huggingface.co/DeepaPeri/MBERT-TRAINED-CONLL2003-en/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://DeepaPeri/MBERT-TRAINED-CONLL2003-en/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/DeepaPeri/MBERT-TRAINED-CONLL2003-en/resolve/main/pytorch_model.bin
539 MB
- Xet hash:
- a8654abafeee0d521629f1e0e0568132522027c99eb406d0d0ca533804d9d81d
- Size of remote file:
- 539 MB
- SHA256:
- 36de80edacc15d43b97c4284f99a5c39d304e498676d7cfcbb52cf0a7f48a045
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