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:
- 8dd2a76285f9588dd958ae4e0925eb81e72ca72399c0fcc8dee012f795cc14fd
- Size of remote file:
- 3.44 kB
- SHA256:
- c23e002918fa93c5061776b48ac6a86d696410473ec0e748f9011a887c68124f
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