This model is BERT-mini (11.3M parameters) fine-tuned for sentence-level sentiment analysis of English literary text using ten ordered sentiment levels (1 = most negative, 10 = most positive). It is one of five student configurations trained on Gemma 4 31B-generated pseudo-labels for literary sentences. The training objective is cumulative-logit ordinal regression, rather than conventional three-class classification. The scalar score is the expected ordinal rating. - Checkpoint selected by minimum validation MAE, with Hemingway annotations kept out of training and selection On the independent human-annotated Hemingway evaluation (n=1,923), this model achieved MAE 0.766, Kendall τ 0.402…
Independent publisher
ARTEM SUSLOV
susurofu
Models in Library1
Datasets in Library0
Models on Hugging Face9
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Published tasks: Text classification.
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