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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NoYo25/BiodivBERT") model = AutoModelForMaskedLM.from_pretrained("NoYo25/BiodivBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f2c107c15d7f4d8d546f5e891c4b37c56523427c41434fd6384a801c36751d7
- Size of remote file:
- 433 MB
- SHA256:
- ac8933c5f2fa626bd8a7c4527c90cc75b17bdf19acce223928e45b089379a420
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