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
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README.md
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output = model(**encoded_input)
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```
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## Training procedure
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I trained on the dataset on the [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased).
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output = model(**encoded_input)
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```
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## Training procedure
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I trained on the stsb_multi_mt(es) dataset on the [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased).
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