Text Classification
Transformers
PyTorch
Safetensors
Spanish
roberta
spanish
bertin
text-embeddings-inference
Instructions to use somosnlp-hackathon-2022/readability-es-paragraphs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use somosnlp-hackathon-2022/readability-es-paragraphs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="somosnlp-hackathon-2022/readability-es-paragraphs")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("somosnlp-hackathon-2022/readability-es-paragraphs") model = AutoModelForSequenceClassification.from_pretrained("somosnlp-hackathon-2022/readability-es-paragraphs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from somosnlp-hackathon-2022/readability-es-paragraphs: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/somosnlp-hackathon-2022/readability-es-paragraphs/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://somosnlp-hackathon-2022/readability-es-paragraphs/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/somosnlp-hackathon-2022/readability-es-paragraphs/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- c50554deb99acd42f4292f9fed12dbff41e9a4391cc76a7b23c115a15d27b07a
- Size of remote file:
- 499 MB
- SHA256:
- 5381bcb229436078e9d194d0c6d0fca8277e0ee3600ce5cc6866d522b937eea8
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