Token Classification
Transformers
PyTorch
English
bert
fill-mask
bert-base-cased
biodiversity
sequence-classification
Instructions to use NoYo25/BiodivBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NoYo25/BiodivBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="NoYo25/BiodivBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NoYo25/BiodivBERT") model = AutoModelForMaskedLM.from_pretrained("NoYo25/BiodivBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from NoYo25/BiodivBERT: direct link, hf CLI and curl.
- Browser
- Download file 669 kB
-
https://huggingface.co/NoYo25/BiodivBERT/resolve/58b4686651b3ff66335a65fbbfe02b1c71794e8a/tokenizer.json
- Command line
-
hf download hf://NoYo25/BiodivBERT@58b4686651b3ff66335a65fbbfe02b1c71794e8a/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/NoYo25/BiodivBERT/resolve/58b4686651b3ff66335a65fbbfe02b1c71794e8a/tokenizer.json
669 kB
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