<jupyter_start><jupyter_text><jupyter_code>!pip install transformers
from transformers import pipeline<jupyter_output><empty_output><jupyter_text>Fill Mask  distilroberta-base (default)<jupyter_code>filler = pipeline('fill-mask')
filler('I am Iron <mask>')<jupyter_output><empty_output><jupyter_text>bert-base-uncased<jupyter_code>filler = pipeline('fill-mask', model = 'bert-base-uncased')
filler('I am Iron [MASK]')<jupyter_output><empty_output><jupyter_text>bert-large-uncased<jupyter_code>filler = pipeline('fill-mask', model = 'bert-large-uncased')
filler('I am Iron [MASK]')<jupyter_output><empty_output><jupyter_text>Named Entity Recognition  dbmdz/bert-large-cased-finetuned-conll03-english<jupyter_code>ner = pipeline('ner', grouped_entities=True)
ner("I am <NAME> from Chennai, Tamil Nadu, India. I am currently learning Hugging Face course.")<jupyter_output><empty_output><jupyter_text>dslim/bert-base-NER<jupyter_code>ner = pipeline('ner', model='dslim/bert-base-NER', grouped_entities=True)
ner("I am <NAME> from Chennai, Tamil Nadu, India. I am currently learning Hugging Face course.")<jupyter_output><empty_output><jupyter_text>Question Answering<jupyter_code><jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13598673300165837, 'fraction_numerical': 0.001658374792703151, 'mean_word_length': 10.495238095238095, 'pattern_counts': {'":': 0, '<': 32, '<?xml version=': 0, '>': 32, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '319966', 'license': 'MIT', 'n_tokens_mistral': 451, 'n_tokens_neox': 439, 'n_words': 68, 'path': '01_Pipeline.ipynb', 'repo_name': 'abdksyed/HuggingFaceCourse', 'size': '1264'}