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testBot001.py # import modules from telegram.ext import (Updater, CommandHandler, MessageHandler, Filters,) import logging from melon_rank import show_music_rank # print(show_music_rank()) print('kase',show_music_rank) # Enable logging logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO) logger = logging.getLogger(__name__) #My bot token from Bot..

In [1]: # conda install -c conda-forge ipywidgets In [2]: from fast_bert.data_cls import BertDataBunch DATA_PATH = './../Downloads/fastai/fast-bert-1.8.0/sample_data/imdb_movie_reviews/data/' LABEL_PATH = './../Downloads/fastai/fast-bert-1.8.0/sample_data/imdb_movie_reviews/label/' OUTPUT_DIR = './../Downloads/fastai/fast-bert-1.8.0/sample_data/imdb_movie_reviews/output/'..

In [1]: import torch from transformers import BertForSequenceClassification model = BertForSequenceClassification.from_pretrained('bert-base-uncased') model.train() Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls...

In [4]: ## BertForMaskedLM from transformers import BertTokenizer, BertForMaskedLM import torch tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertForMaskedLM.from_pretrained('bert-base-uncased') input_ids = tokenizer("Hello, my dog is cute", return_tensors="pt")["input_ids"] # print(input_ids) outputs = model(input_ids, labels=input_ids) loss, prediction_scores = outputs..

In [6]: from __future__ import print_function import ipywidgets as widgets from transformers import pipeline print('success') success In [7]: nlp_sentence_classif = pipeline('sentiment-analysis') nlp_sentence_classif('Such a nice weather outside !') Out[7]: [{'label': 'POSITIVE', 'score': 0.9997655749320984}] In [12]: nlp_token_class = pipeline('ner') nlp_token_class('Hugging Face is a French co..