90 lines
2.3 KiB
Markdown
90 lines
2.3 KiB
Markdown
## Fact checking
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This generative model - trained on FEVER - aims to predict whether a claim is consistent with the provided evidence.
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### Installation and simple usage
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One quick way to install it is to type
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```bash
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pip install fact_checking
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```
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and then use the following code:
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```python
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from transformers import (
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GPT2LMHeadModel,
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GPT2Tokenizer,
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)
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from fact_checking import FactChecker
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_evidence = """
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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.
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"""
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_claim = 'Justine Bateman is a poet.'
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
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fact_checking_model = GPT2LMHeadModel.from_pretrained('fractalego/fact-checking')
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fact_checker = FactChecker(fact_checking_model, tokenizer)
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is_claim_true = fact_checker.validate(_evidence, _claim)
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print(is_claim_true)
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```
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which gives the output
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```bash
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False
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```
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### Probabilistic output with replicas
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The output can include a probabilistic component, obtained by iterating a number of times the output generation.
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The system generates an ensemble of answers and groups them by Yes or No.
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For example, one can ask
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```python
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from transformers import (
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GPT2LMHeadModel,
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GPT2Tokenizer,
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)
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from fact_checking import FactChecker
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_evidence = """
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Jane writes code for Huggingface.
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"""
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_claim = 'Jane is an engineer.'
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
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fact_checking_model = GPT2LMHeadModel.from_pretrained('fractalego/fact-checking')
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fact_checker = FactChecker(fact_checking_model, tokenizer)
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is_claim_true = fact_checker.validate_with_replicas(_evidence, _claim)
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print(is_claim_true)
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```
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with output
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```bash
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{'Y': 0.95, 'N': 0.05}
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```
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### Score on FEVER
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The predictions are evaluated on a subset of the FEVER dev dataset,
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restricted to the SUPPORTING and REFUTING options:
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| precision | recall | F1|
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| --- | --- | --- |
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|0.94|0.98|0.96|
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These results should be taken with many grains of salt. This is still a work in progress,
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and there might be leakage coming from the underlining GPT2 model unnaturally raising the scores.
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