Text Classification
Transformers
PyTorch
English
bert
feature-extraction
exbert
linkbert
fill-mask
question-answering
token-classification
text-embeddings-inference
Instructions to use michiyasunaga/LinkBERT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michiyasunaga/LinkBERT-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="michiyasunaga/LinkBERT-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("michiyasunaga/LinkBERT-base") model = AutoModel.from_pretrained("michiyasunaga/LinkBERT-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0456b4b466f951f87cdf4a8a67f8ae5dee313521d33f2cb07b318d2d268ca05b
- Size of remote file:
- 433 MB
- SHA256:
- 750fd38a2e83812c6259263189a27f7e3673f2e9efaec1fb082e06d65619756b
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