Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use everyl12/user_class_L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use everyl12/user_class_L with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="everyl12/user_class_L")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("everyl12/user_class_L") model = AutoModelForSequenceClassification.from_pretrained("everyl12/user_class_L", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5803c52e364029da7586e1e26ff3331e59433be61d6617ea9ba555999d6f6c21
- Size of remote file:
- 3.39 kB
- SHA256:
- d0c6451929e638082b9c8ec91bdb6a3842765ce56f8141f0e3f0699c861256e0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.