Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HCKLab/BiBert-MultiTask-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HCKLab/BiBert-MultiTask-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HCKLab/BiBert-MultiTask-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HCKLab/BiBert-MultiTask-1") model = AutoModelForSequenceClassification.from_pretrained("HCKLab/BiBert-MultiTask-1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db01c346fe6fa8ac2d61fd8a6b2d9467abe8524d1762585f76bc680be9b043e5
- Size of remote file:
- 670 MB
- SHA256:
- 20d43f83168e982ed4571a9278380f079d79ff43dfa9c4e8b01ee53451dbcec8
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