Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use platzi/platzi-distilroberta-base-mrpc-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use platzi/platzi-distilroberta-base-mrpc-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="platzi/platzi-distilroberta-base-mrpc-test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("platzi/platzi-distilroberta-base-mrpc-test") model = AutoModelForSequenceClassification.from_pretrained("platzi/platzi-distilroberta-base-mrpc-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from platzi/platzi-distilroberta-base-mrpc-test: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/platzi/platzi-distilroberta-base-mrpc-test/resolve/main/training_args.bin
- Command line
-
hf download hf://platzi/platzi-distilroberta-base-mrpc-test/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/platzi/platzi-distilroberta-base-mrpc-test/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- de3412ba01a2841dcf8ab64790e2b2105acafc14c8234f4aead61e85025527e2
- Size of remote file:
- 3.58 kB
- SHA256:
- 2ee36274de2d85c7cac73a7a1da8f10a93c6fa6e541c5af8f8a4af3f5bce6e6d
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