Instructions to use hiiamsid/BETO_es_binary_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hiiamsid/BETO_es_binary_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hiiamsid/BETO_es_binary_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hiiamsid/BETO_es_binary_classification") model = AutoModelForSequenceClassification.from_pretrained("hiiamsid/BETO_es_binary_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a21ec2c123f51ab61ba9bc32ae58f466a3d1dc924b6d79272484f73bdee6f4c
- Size of remote file:
- 439 MB
- SHA256:
- 44795eb45f647119272e626cf5ce87759e31c6fee00fd25b38765edaa1356df0
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