Instructions to use pachequinho/sentiment_bert_restaurant_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pachequinho/sentiment_bert_restaurant_10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pachequinho/sentiment_bert_restaurant_10")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pachequinho/sentiment_bert_restaurant_10") model = AutoModelForSequenceClassification.from_pretrained("pachequinho/sentiment_bert_restaurant_10") - Notebooks
- Google Colab
- Kaggle
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# sentiment_bert_restaurant_10
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the
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It achieves the following results on the evaluation set:
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- Loss: 0.0755
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- Accuracy: 0.9867
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# sentiment_bert_restaurant_10
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the [pachequinho/restaurant_reviews](https://huggingface.co/datasets/pachequinho/restaurant_reviews) dataset with only 10% of the training data.
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It achieves the following results on the evaluation set:
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- Loss: 0.0755
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- Accuracy: 0.9867
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