YAML Metadata Warning: The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

flan-t5-base-pt2 — Essay Trait Scorer

This model is a fine-tuned version of google/flan-t5-base designed for trait-based essay scoring. It was trained on Sets 7 & 8 of the ASAP AES dataset, extended with floating-point scoring and GPT validation.

Each input includes:

  • a trait (e.g., Ideas, Organization),
  • the relevant rubric definition,
  • and the student's essay.

The model outputs a trait-specific score between 0.0 and 3.0, which can be scaled to a final grade out of 100.

🧠 Scoring is optionally cross-validated with OpenAI’s GPT for more reliable results.

Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("srutiii/flan-t5-base-pt2")
model = AutoModelForSeq2SeqLM.from_pretrained("srutiii/flan-t5-base-pt2")
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