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Quick and dirty hack for binary movie sentiment analysis.
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Finetuned with LoRA on ericzzz/falcon-rw-1b-instruct-openorca.
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https://huggingface.co/datasets/open-llm-leaderboard/details_ericzzz__falcon-rw-1b-instruct-openorca
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Trained on a subset of "IMDB Dataset of 50K Movie Reviews" from Kaggle:
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https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews
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Input: String of a movie review (e.g. "\<human\>: This movie sucks, I'd rather stay at home! \<assistant\>:")
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Output: String containing the sentiment (e.g. "... negative \<assistant\>: negative \<assistant\>: negative ...")
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### Framework versions
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- PEFT 0.7.1
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Quick and dirty hack for binary movie sentiment analysis.
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Finetuned with LoRA on ericzzz/falcon-rw-1b-instruct-openorca.
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https://huggingface.co/datasets/open-llm-leaderboard/details_ericzzz__falcon-rw-1b-instruct-openorca
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Trained on a subset of "IMDB Dataset of 50K Movie Reviews" from Kaggle:
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https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews
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### Input / Output
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Input: String of a movie review (e.g. "\<human\>: This movie sucks, I'd rather stay at home! \<assistant\>:")
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Output: String containing the sentiment (e.g. "... negative \<assistant\>: negative \<assistant\>: negative ...")
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### Example Google Colab Code
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https://colab.research.google.com/drive/1LUILztSocpqpMz8xACbtmxl-W-cORXRZ?usp=sharing
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### Framework versions
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- PEFT 0.7.1
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