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:
- 6f2e9208e9ec342a50b06efa949c1f20c0bb10b37e4d3e3779674006891a1e8b
- Size of remote file:
- 433 MB
- SHA256:
- ef41191ff24b73e071594c26e9d29412dc517110ecce6e2f7bbaaa19da33d653
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