Text Classification
Safetensors
GLiClass
text classification
nli
sentiment analysis
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  <!-- Provide a quick summary of what the model is/does. -->
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  ## Model Details
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  <!-- Provide a quick summary of what the model is/does. -->
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+ SetFit/CR
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+ {'micro': 0.9175531914893617, 'macro': 0.9126596980255517, 'weighted': 0.918377937578768}
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+ SetFit/sst2
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+ {'micro': 0.8962108731466227, 'macro': 0.8958969353374626, 'weighted': 0.8959063534717374}
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+ SetFit/sst5
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+ {'micro': 0.4425339366515837, 'macro': 0.3376055122341758, 'weighted': 0.37569438642720765}
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+ AmazonScience/massive
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+ {'micro': 0.49630127774041694, 'macro': 0.5039863928641198, 'weighted': 0.5157040017835929}
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+ stanfordnlp/imdb
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+ positive ['negative', 'positive']
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+ {'micro': 0.9252, 'macro': 0.9251093043719667, 'weighted': 0.925111389328933}
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+ SetFit/20_newsgroups
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+ {'micro': 0.477562400424854, 'macro': 0.475900615057257, 'weighted': 0.48754377671231747}
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+ SetFit/enron_spam
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+ {'micro': 0.676, 'macro': 0.6759841232220378, 'weighted': 0.6759659783329384}
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+ PolyAI/banking77
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+ {'micro': 0.45454545454545453, 'macro': 0.4698065141317343, 'weighted': 0.46980651413173424}
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+ takala/financial_phrasebank
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+ {'micro': 0.916077738515901, 'macro': 0.8971295837169703, 'weighted': 0.9146016290019535}
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+ ag_news
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+ {'micro': 0.7475, 'macro': 0.7278530550758112, 'weighted': 0.7278530550758112}
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+ dair-ai/emotion
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+ {'micro': 0.516, 'macro': 0.4447390851981113, 'weighted': 0.5260998899966529}
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+ MoritzLaurer/cap_sotu
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+ {'micro': 0.4708, 'macro': 0.46143322893111716, 'weighted': 0.4788448480172241}
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+ cornell-movie-review-data/rotten_tomatoes
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+ {'micro': 0.7954971857410882, 'macro': 0.7942799070847851, 'weighted': 0.7942799070847851}
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+ Average Score: 0.655567996557777
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  ## Model Details
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