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bert-base-cased-qa-evaluator

iarfmoose

This model takes a question answer pair as an input and outputs a value representing its prediction about whether the input was a valid question and answer pair or not. The model is a pretrained BERT-base-cased with a sequence classification head. The QA evaluator was originally designed to be used with the t5-base-question-generator for evaluating the quality of generated questions. The input for the QA evaluator follows the format for `BertForSequenceClassification`, but using the question and answer as the two sequences. Inputs should take the following format:

Text analysis

Input Text→ Output Text

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t5-base-question-generator

iarfmoose

This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output. It is based on a pretrained `t5-base` model. The model is trained to generate reading comprehension-style questions with answers extracted from a text. The model performs best with full sentence answers, but can also be used with single word or short phrase answers. The model takes concatenated answers and context as an input sequence, and will generate a full question sentence as an output sequence. The max sequence length is 512 tokens. Inputs should be organised into the following format:

Category pending

Input Not verified→ Output Not verified

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Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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