Instructions to use dejanseo/sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dejanseo/sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dejanseo/sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dejanseo/sentiment") model = AutoModelForSequenceClassification.from_pretrained("dejanseo/sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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pipeline_tag: text-classification
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widget:
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- example_title: "Very Positive"
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text: "I recently bought a new sports jersey from this store, and it's absolutely amazing! The fabric is high quality, the fit is perfect, and the design looks even better in person. I'm thrilled with my purchase and highly recommend this store to anyone looking for custom sports apparel."
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pipeline_tag: text-classification
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widget:
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- example_title: "Very Positive"
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text: "This product is absolutely amazing!"
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- example_title: "Positive"
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text: "I like the features of this app."
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- example_title: "Somewhat Positive"
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text: "The service is pretty good."
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- example_title: "Neutral"
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text: "It meets my expectations."
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- example_title: "Somewhat Negative"
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text: "The experience was not as bad."
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- example_title: "Negative"
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text: "I'm disappointed with the quality."
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- example_title: "Very Negative"
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text: "This is the worst purchase I've ever made."
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- example_title: "Very Positive"
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text: "I recently bought a new sports jersey from this store, and it's absolutely amazing! The fabric is high quality, the fit is perfect, and the design looks even better in person. I'm thrilled with my purchase and highly recommend this store to anyone looking for custom sports apparel."
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