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--- |
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pipeline_tag: text-generation |
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--- |
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# Factual Consistency Evaluator/Metric in ACL 2023 paper |
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*[WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning |
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](https://arxiv.org/abs/2212.10057)* |
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Open-sourced code: https://github.com/nightdessert/WeCheck |
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## Model description |
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WeCheck is a factual consistency metric trained from weakly annotated samples. |
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This WeCheck checkpoint can be used to check the following three generation tasks: |
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**Text Summarization/Knowlege grounded dialogue Generation/Paraphrase** |
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This WeCheck checkpoint is trained based on the following three weak labler: |
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*[QAFactEval |
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](https://github.com/salesforce/QAFactEval)* / *[Summarc](https://github.com/tingofurro/summac)* / *[NLI warmup](https://huggingface.co/MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli)* |
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--- |
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# How to use the model |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") |
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model_name = "nightdessert/WeCheck" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing." # Input for Summarization/ Dialogue / Paraphrase |
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hypothesis = "The movie was not good." # Output for Summarization/ Dialogue / Paraphrase |
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input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt", truncation_strategy="only_first", max_length=512) |
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output = model(input["input_ids"].to(device))['logits'][:,0] # device = "cuda:0" or "cpu" |
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prediction = torch.sigmoid(output).tolist() |
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print(prediction) #0.884 |
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``` |
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or apply for a batch of samples |
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```python |
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premise = ["I first thought that I liked the movie, but upon second thought it was actually disappointing."]*3 # Input list for Summarization/ Dialogue / Paraphrase |
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hypothesis = ["The movie was not good."]*3 # Output list for Summarization/ Dialogue / Paraphrase |
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batch_tokens = tokenizer.batch_encode_plus(list(zip(premise, hypothesis)), padding=True, |
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truncation=True, max_length=512, return_tensors="pt", truncation_strategy="only_first") |
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output = model(batch_tokens["input_ids"].to(device))['logits'][:,0] # device = "cuda:0" or "cpu" |
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prediction = torch.sigmoid(output).tolist() |
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print(prediction) #[0.884,0.884,0.884] |
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``` |
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license: openrail |
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pipeline_tag: text-classification |
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tags: |
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- Factual Consistency |
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- Natrual Language Inference |
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--- |
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language: |
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- en |
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tags: |
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- Factual Consistency Evaluation |
