Instructions to use puzzz21/sci-sentiment-classify with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use puzzz21/sci-sentiment-classify with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="puzzz21/sci-sentiment-classify")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("puzzz21/sci-sentiment-classify") model = AutoModelForSequenceClassification.from_pretrained("puzzz21/sci-sentiment-classify") - Notebooks
- Google Colab
- Kaggle
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README.md
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pipeline_tag: text-classification
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This model
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pipeline_tag: text-classification
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This model is finetuned on Scibert.
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It classifies sentiment of the scientific text based on it's context.
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Here, the context is the sentiment of the author depicted in the sentence.
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There are three classes; postitive, negative and neutral.
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This model outputs following classnames according to the sentiment:
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p = positive
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n = negative
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o = other/neutral
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For finetuning, the publicly available dataset on context identification from Angrosh et al. https://dl.acm.org/doi/10.1145/1816123.1816168 is used.
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