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README.md
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The training dataset used is the English Language Learner Insight, Proficiency, and Skills Evaluation (ELLIPSE) Corpus.
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This freely available resource comprises approximately 6,500 writing composition samples from English language learners,
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each scored for overall holistic language proficiency as well as analytic scores pertaining to cohesion, syntax, vocabulary,
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phraseology, grammar, and conventions. The scores were obtained through assessments by a number of professional English teachers
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The model's performance on the test dataset, which includes around 980 English essays, is summarized by the following metrics: 'accuracy'= 0.87 and 'f1 score' = 0.85.
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Upon inputting an essay, the model outputs six scores corresponding to cohesion, syntax, vocabulary, phraseology, grammar, and conventions. Each score ranges from 1 to 5, with higher scores indicating greater proficiency within the essay. These dimensions collectively assess the quality of the input essay from multiple perspectives. The model serves as a valuable tool for EFL teachers and researchers, and it is also beneficial for English L2 learners and parents for self-evaluating their composition skills.
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To test the model, run the following code or paste your essay into the API interface:
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#import packages
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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The training dataset used is the English Language Learner Insight, Proficiency, and Skills Evaluation (ELLIPSE) Corpus.
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This freely available resource comprises approximately 6,500 writing composition samples from English language learners,
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each scored for overall holistic language proficiency as well as analytic scores pertaining to cohesion, syntax, vocabulary,
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phraseology, grammar, and conventions. The scores were obtained through assessments by a number of professional English teachers
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adhering to rigorous procedures. The training dataset guarantees that our model acuqires high practicality and accuracy, closely emulating professional grading standards.
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The model's performance on the test dataset, which includes around 980 English essays, is summarized by the following metrics: 'accuracy'= 0.87 and 'f1 score' = 0.85.
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Upon inputting an essay, the model outputs six scores corresponding to cohesion, syntax, vocabulary, phraseology, grammar, and conventions. Each score ranges from 1 to 5, with higher scores indicating greater proficiency within the essay. These dimensions collectively assess the quality of the input essay from multiple perspectives. The model serves as a valuable tool for EFL teachers and researchers, and it is also beneficial for English L2 learners and parents for self-evaluating their composition skills.
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To test the model, run the following code or paste your essay into the API interface:
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```
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#import packages
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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