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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HCKLab/BiBert-MultiTask-2") model = AutoModelForSequenceClassification.from_pretrained("HCKLab/BiBert-MultiTask-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from HCKLab/BiBert-MultiTask-2: direct link, hf CLI and curl.
- Browser
- Download file 670 MB
-
https://huggingface.co/HCKLab/BiBert-MultiTask-2/resolve/1c87edcd9d04814a416fc43b1e343dc41f7546bb/pytorch_model.bin
- Command line
-
hf download hf://HCKLab/BiBert-MultiTask-2@1c87edcd9d04814a416fc43b1e343dc41f7546bb/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/HCKLab/BiBert-MultiTask-2/resolve/1c87edcd9d04814a416fc43b1e343dc41f7546bb/pytorch_model.bin
670 MB
- Xet hash:
- 5d49df81ea5d4fe7e27cc85343d5ab76335faedfa533461d17070e68cfd6b73d
- Size of remote file:
- 670 MB
- SHA256:
- 9d8028a35a8b177ab5fda85cceb5046006968a9d65f1ce8252164e5aa3d4deb3
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