Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HCKLab/BiBert-Classification-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HCKLab/BiBert-Classification-V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HCKLab/BiBert-Classification-V2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HCKLab/BiBert-Classification-V2") model = AutoModelForSequenceClassification.from_pretrained("HCKLab/BiBert-Classification-V2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9351c460dfce1f8415f6ee52d2f78b0f3baf310cf0d4162404c07f26756c0a53
- Size of remote file:
- 670 MB
- SHA256:
- 5c680939a8c200550e48569df51c0ef6caef33f05a92811489ebfadcf4bbdaf9
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