mirror of
https://github.com/tanyuqian/knowledge-harvest-from-lms.git
synced 2023-06-02 01:35:42 +03:00
134 lines
4.4 KiB
Python
134 lines
4.4 KiB
Python
import fire
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import json
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from nltk import sent_tokenize
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from thefuzz import fuzz
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from models.gpt3 import GPT3
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from data_utils.data_utils import get_n_ents, get_sent, fix_prompt_style
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TRANSFORMATIONS_SENT = [['', ''], ['a ', ''], ['the ', '']]
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TRANSFORMATIONS_ENT = [
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['', ''], ['being', 'is'], ['being', 'are'], ['ing', ''], ['ing', 'e']]
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def get_paraphrase_prompt(gpt3, prompt, ent_tuple):
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assert get_n_ents(prompt) == len(ent_tuple)
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ent_tuple = [ent.lower() for ent in ent_tuple]
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sent = get_sent(prompt=prompt, ent_tuple=ent_tuple)
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for _ in range(5):
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raw_response = gpt3.call(prompt=f'paraphrase:\n{sent}\n')
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para_sent = raw_response['choices'][0]['text']
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para_sent = sent_tokenize(para_sent)[0]
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para_sent = para_sent.strip().strip('.').lower()
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print('para_sent:', para_sent)
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prompt = para_sent
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valid = True
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for idx, ent in enumerate(ent_tuple):
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# prompt = prompt.replace(ent, f'<ENT{idx}>')
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for trans_sent in TRANSFORMATIONS_SENT:
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for trans_ent in TRANSFORMATIONS_ENT:
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if prompt.count(f'<ENT{idx}>') == 0:
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transed_prompt = prompt.replace(*trans_sent)
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transed_ent = ent.replace(*trans_ent)
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if transed_prompt.count(transed_ent) == 1:
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prompt = transed_prompt.replace(
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transed_ent, f'<ENT{idx}>')
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if prompt.count(f'<ENT{idx}>') != 1:
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valid = False
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break
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if valid:
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return prompt
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return None
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def search_prompts(init_prompts, seed_ent_tuples, similarity_threshold):
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gpt3 = GPT3()
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cache = {}
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prompts = []
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while True:
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new_prompts = []
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for prompt in init_prompts + init_prompts + prompts:
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for ent_tuple in seed_ent_tuples:
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ent_tuple = [ent.replace('_', ' ') for ent in ent_tuple]
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request_str = f'{prompt} ||| {ent_tuple}'
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if request_str not in cache or prompt in init_prompts:
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cache[request_str] = get_paraphrase_prompt(
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gpt3=gpt3, prompt=prompt, ent_tuple=ent_tuple)
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para_prompt = cache[request_str]
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print(f'prompt: {prompt}\tent_tuple: {ent_tuple}'
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f'\t-> para_prompt: {para_prompt}')
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if para_prompt is not None and \
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para_prompt not in init_prompts + prompts:
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new_prompts.append(para_prompt)
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if len(set(prompts + new_prompts)) >= 20:
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break
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if len(new_prompts) == 0:
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break
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else:
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# prompts.extend(new_prompts)
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flag = False
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for new_prompt in sorted(new_prompts, key=lambda t: len(t)):
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if len(prompts) != 0:
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max_sim = max([fuzz.ratio(new_prompt, prompt)
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for prompt in prompts])
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print(f'-- {new_prompt}: {max_sim}')
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if len(prompts) == 0 or \
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max([fuzz.ratio(new_prompt, prompt)
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for prompt in prompts]) < similarity_threshold:
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prompts.append(new_prompt)
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flag = True
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prompts = list(set(prompts))
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prompts.sort(key=lambda s: len(s))
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if len(prompts) >= 10 or flag == False:
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break
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for i in range(len(prompts)):
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prompts[i] = fix_prompt_style(prompts[i])
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return prompts
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def main(rel_set='conceptnet', similarity_threshold=75):
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relation_info = json.load(open(f'relation_info/{rel_set}.json'))
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for rel, info in relation_info.items():
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info['init_prompts'] = [
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fix_prompt_style(prompt) for prompt in info['init_prompts']]
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if 'prompts' not in info or len(info['prompts']) == 0:
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info['prompts'] = search_prompts(
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init_prompts=info['init_prompts'],
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seed_ent_tuples=info['seed_ent_tuples'],
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similarity_threshold=similarity_threshold)
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for key, value in info.items():
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print(f'{key}: {value}')
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for prompt in info['prompts']:
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print(f'- {prompt}')
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print('=' * 50)
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output_path = f'relation_info/{rel_set}.json'
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json.dump(relation_info, open(output_path, 'w'), indent=4)
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if __name__ == '__main__':
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fire.Fire(main)
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