Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use mpreda/test_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mpreda/test_dir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mpreda/test_dir")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mpreda/test_dir") model = AutoModelForSequenceClassification.from_pretrained("mpreda/test_dir", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 237373226db4d11e1ee2d7be6c20d60f28afbf6508f60030f57fbe77c6278d2d
- Size of remote file:
- 5.78 kB
- SHA256:
- 0226b022a73e1770dd90d677cda86827e8a023bf0f2005cc37865f7ec3c212dd
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