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:
- a27c1e16817e290b67f396ae9279810775c6ec7f318a91615652972496cee396
- Size of remote file:
- 670 MB
- SHA256:
- bb5ad4d5e37d2a8b19b3695d18a20823aef30ca3414e17ce4c54698dbef48a2e
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