Instructions to use Vamsi11/sentiment_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vamsi11/sentiment_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Vamsi11/sentiment_analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Vamsi11/sentiment_analysis") model = AutoModelForSequenceClassification.from_pretrained("Vamsi11/sentiment_analysis") - Notebooks
- Google Colab
- Kaggle
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# test_trainer
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on
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It achieves the following results on the evaluation set:
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- Loss: 0.6039
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- Accuracy: 0.9038
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# test_trainer
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on imdb dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6039
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- Accuracy: 0.9038
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