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
adding pytorch model
Browse files- pytorch_model.bin +3 -0
pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:44795eb45f647119272e626cf5ce87759e31c6fee00fd25b38765edaa1356df0
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size 439493805
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