Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HCKLab/BiBert-MultiTask-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HCKLab/BiBert-MultiTask-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HCKLab/BiBert-MultiTask-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HCKLab/BiBert-MultiTask-2") model = AutoModelForSequenceClassification.from_pretrained("HCKLab/BiBert-MultiTask-2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bba618d0bfde7f5f9e3f83c321e020afc6a07bb43f9fb41106f77852c7dcf565
- Size of remote file:
- 3.44 kB
- SHA256:
- 3ce41606b8ada62f1324218e4a426e2e7624c83eb309edacf62acba38e1f1003
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