Instructions to use gplsi/Constructive_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gplsi/Constructive_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gplsi/Constructive_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gplsi/Constructive_model") model = AutoModelForSequenceClassification.from_pretrained("gplsi/Constructive_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from gplsi/Constructive_model: direct link, hf CLI and curl.
- Browser
- Download file 234 Bytes
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https://huggingface.co/gplsi/Constructive_model/resolve/main/README.md
- Command line
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hf download hf://gplsi/Constructive_model/README.md
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curl -L -o README.md https://huggingface.co/gplsi/Constructive_model/resolve/main/README.md
234 Bytes
metadata
license: apache-2.0
language:
- es
Comment classification model in constructive or not. This model has been obtained by fine-tuning the RoBERTa language model in Spanish (https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne)