Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use asvs/qs-classifier-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asvs/qs-classifier-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="asvs/qs-classifier-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("asvs/qs-classifier-bert") model = AutoModelForSequenceClassification.from_pretrained("asvs/qs-classifier-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63f049d71e7fc092854dff731e675c36088f0f021a61b83408d9e08fdcf965c4
- Size of remote file:
- 438 MB
- SHA256:
- c75dce0f2c95fd4ac746528f409a28c3884396196ec754eb3f57c20f2788c03f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.