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:
- aed3c98160e4c66cec60e31eed75c9efb798aac21a8a081cdb14186e5cca69f1
- Size of remote file:
- 3.06 kB
- SHA256:
- 04ed0483362194e67a2d52d6a1a6d7cd93f04dc5a8464e72a20a80ee4473a782
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.