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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HCKLab/BiBert-Subjectivity") model = AutoModelForSequenceClassification.from_pretrained("HCKLab/BiBert-Subjectivity") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 102efa53f66cd872acfc961bdd8bcd6ebd5cbd87cfb2db237f026978a9cd1b83
- Size of remote file:
- 3.45 kB
- SHA256:
- b41369121932afa9c89c62782a39a37dce448234419fd7ed4c76dc09bc8485c7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.