| ## Fact checking | |
| This generative model - trained on FEVER - aims to predict whether a claim is consistent with the provided evidence. | |
| ### Installation and simple usage | |
| One quick way to install it is to type | |
| ```bash | |
| pip install fact_checking | |
| ``` | |
| and then use the following code: | |
| ```python | |
| from transformers import ( | |
| GPT2LMHeadModel, | |
| GPT2Tokenizer, | |
| ) | |
| from fact_checking import FactChecker | |
| _evidence = """ | |
| Justine Tanya Bateman (born February 19, 1966) is an American writer, producer, and actress . She is best known for her regular role as Mallory Keaton on the sitcom Family Ties (1982 -- 1989). Until recently, Bateman ran a production and consulting company, SECTION 5 . In the fall of 2012, she started studying computer science at UCLA. | |
| """ | |
| _claim = 'Justine Bateman is a poet.' | |
| tokenizer = GPT2Tokenizer.from_pretrained('gpt2') | |
| fact_checking_model = GPT2LMHeadModel.from_pretrained('fractalego/fact-checking') | |
| fact_checker = FactChecker(fact_checking_model, tokenizer) | |
| is_claim_true = fact_checker.validate(_evidence, _claim) | |
| print(is_claim_true) | |
| ``` | |
| which gives the output | |
| ```bash | |
| False | |
| ``` | |
| ### Probabilistic output with replicas | |
| The output can include a probabilistic component, obtained by iterating a number of times the output generation. | |
| The system generates an ensemble of answers and groups them by Yes or No. | |
| For example, one can ask | |
| ```python | |
| from transformers import ( | |
| GPT2LMHeadModel, | |
| GPT2Tokenizer, | |
| ) | |
| from fact_checking import FactChecker | |
| _evidence = """ | |
| Jane writes code for Huggingface. | |
| """ | |
| _claim = 'Jane is an engineer.' | |
| tokenizer = GPT2Tokenizer.from_pretrained('gpt2') | |
| fact_checking_model = GPT2LMHeadModel.from_pretrained('fractalego/fact-checking') | |
| fact_checker = FactChecker(fact_checking_model, tokenizer) | |
| is_claim_true = fact_checker.validate_with_replicas(_evidence, _claim) | |
| print(is_claim_true) | |
| ``` | |
| with output | |
| ```bash | |
| {'Y': 0.95, 'N': 0.05} | |
| ``` | |
| ### Score on FEVER | |
| The predictions are evaluated on a subset of the FEVER dev dataset, | |
| restricted to the SUPPORTING and REFUTING options: | |
| | precision | recall | F1| | |
| | --- | --- | --- | | |
| |0.94|0.98|0.96| | |
| These results should be taken with many grains of salt. This is still a work in progress, | |
| and there might be leakage coming from the underlining GPT2 model unnaturally raising the scores. | |