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license: cc-by-nc-4.0 |
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This is a **Factual Consistency Evaluation** model, introduced in the [TrueTeacher paper (Gekhman et al, 2023)](https://arxiv.org/pdf/2305.11171.pdf). |
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The model is optimized for evaluating factual consistency in **summarization**. |
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It is the main model from the paper (see **"T5-11B w. ANLI + TrueTeacher full"** in **Table 1**) which is based on a **T5-11B** fine-tuned with a mixture of the following datasets: |
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- TrueTeacher ([Gekhman et al., 2023](https://arxiv.org/pdf/2305.11171.pdf)) |
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- ANLI ([Nie et al., 2020](https://aclanthology.org/2020.acl-main.441.pdf)) |
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The input format for the model is: "premise: GROUNDING_DOCUMENT hypothesis: HYPOTHESIS_SUMMARY". |
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The model predicts a binary label ('1' - Factualy Consistent, '0' - Factualy Inconsistent). |
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## Usage example: |
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```python |
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from transformers import T5ForConditionalGeneration |
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from transformers import T5Tokenizer |
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model_path = 'google/t5_11b_trueteacher_and_anli' |
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tokenizer = T5Tokenizer.from_pretrained(model_path) |
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model = T5ForConditionalGeneration.from_pretrained(model_path) |
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premise = 'the sun is shining' |
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for hypothesis, expected in [('the sun is out in the sky', '1'), |
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('the cat is shiny', '0')]: |
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input_ids = tokenizer(f'premise: {premise} hypothesis: {hypothesis}', return_tensors='pt').input_ids |
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outputs = model.generate(input_ids) |
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result = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(f'premise: {premise}') |
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print(f'hypothesis: {hypothesis}') |
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print(f'result: {result} (expected: {expected})\n') |
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``` |
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## Citation |
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If you use this model for a research publication, please cite the TrueTeacher paper (using the bibtex entry below) and the dataset papers mentioned above. |
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``` |
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@misc{gekhman2023trueteacher, |
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title={TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models}, |
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author={Zorik Gekhman and Jonathan Herzig and Roee Aharoni and Chen Elkind and Idan Szpektor}, |
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year={2023}, |
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eprint={2305.11171}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |