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 model.safetensors from HCKLab/BiBert-MultiTask-2: direct link, hf CLI and curl.
- Browser
- Download file 669 MB
-
https://huggingface.co/HCKLab/BiBert-MultiTask-2/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://HCKLab/BiBert-MultiTask-2@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/HCKLab/BiBert-MultiTask-2/resolve/refs%2Fpr%2F1/model.safetensors
669 MB
- Xet hash:
- 91722418dca1a73796fff20879b9c5c9708710615149dce6fa0af46ce46a501e
- Size of remote file:
- 669 MB
- SHA256:
- 7f28a935d77ffd4fed104815f364f712d147a8bae7ea5d2bcb7c6ef11039e577
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.