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 pytorch_model.bin from platzi/platzi-distilroberta-base-mrpc-test: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/platzi/platzi-distilroberta-base-mrpc-test/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://platzi/platzi-distilroberta-base-mrpc-test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/platzi/platzi-distilroberta-base-mrpc-test/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- e42e7c29e60c1f04f215c2c5033f7297d183c02e37ba96338d3c63dab823fc88
- Size of remote file:
- 329 MB
- SHA256:
- bfe2800a5693e629ef22b3177edbbf21f2b4e1026c966b6911cf429d6a890240
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