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1# tested in transformers==4.53.0
2from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline
3
4maskesg = AutoModelForMaskedLM.from_pretrained('nguyen599/MaskESG-DistilBERT-base')
5tokenizer = AutoTokenizer.from_pretrained('nguyen599/MaskESG-DistilBERT-base')
6nlp = pipeline("fill-mask", model=maskesg, tokenizer=tokenizer)
7# Classification as fill-mask
8results = nlp(f'Over three chapters, it covers a range of topics from energy efficiency and renewable energy to the circular economy and sustainable transportation. This sentence is {tokenizer.mask_token}')
9print(results)
10# [{'score': 0.9015821814537048,
11# 'token': 444,
12# 'token_str': ' E',
13# 'sequence': 'Over three chapters, it covers a range of topics from energy efficiency and renewable energy to the circular economy and sustainable transportation. This sentence is E'},
14# {'score': 0.09723947197198868,
15# 'token': 427,
16# 'token_str': ' N',
17# 'sequence': 'Over three chapters, it covers a range of topics from energy efficiency and renewable energy to the circular economy and sustainable transportation. This sentence is N'},
18# {'score': 0.0010556845227256417,
19# 'token': 322,
20# 'token_str': ' S',
21# 'sequence': 'Over three chapters, it covers a range of topics from energy efficiency and renewable energy to the circular economy and sustainable transportation. This sentence is S'},
22# {'score': 0.0001152529803221114,
23# 'token': 443,
24# 'token_str': ' G',
25# 'sequence': 'Over three chapters, it covers a range of topics from energy efficiency and renewable energy to the circular economy and sustainable transportation. This sentence is G'},
26# {'score': 1.14425779429439e-06,
27# 'token': 299,
28# 'token_str': ' e',
29# 'sequence': 'Over three chapters, it covers a range of topics from energy efficiency and renewable energy to the circular economy and sustainable transportation. This sentence is e'}]
30