Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HCKLab/BiBert-Subjectivity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HCKLab/BiBert-Subjectivity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HCKLab/BiBert-Subjectivity")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HCKLab/BiBert-Subjectivity") model = AutoModelForSequenceClassification.from_pretrained("HCKLab/BiBert-Subjectivity", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from HCKLab/BiBert-Subjectivity: direct link, hf CLI and curl.
- Browser
- Download file 670 MB
-
https://huggingface.co/HCKLab/BiBert-Subjectivity/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://HCKLab/BiBert-Subjectivity@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/HCKLab/BiBert-Subjectivity/resolve/refs%2Fpr%2F1/pytorch_model.bin
670 MB
- Xet hash:
- 871434cd4e8209f440f55901b6b7a23d6c25da3c462d556d45ea93582cc06d9b
- Size of remote file:
- 670 MB
- SHA256:
- 6bcfca4e9ec4755e733c581c5c53cbdd099827b58a8252d95bb5921dc6ea01ef
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