Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use davidgaofc/temp_attack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davidgaofc/temp_attack with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="davidgaofc/temp_attack")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("davidgaofc/temp_attack") model = AutoModelForSequenceClassification.from_pretrained("davidgaofc/temp_attack") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dc8e0e29a0e328e73145fdd18568619cd83a1199e0c5142a637c553b67034772
- Size of remote file:
- 268 MB
- SHA256:
- 6559a53aa18d9637cbe118e00db2409c96836cf15512eb17e40bdf38a0bdf145
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