kusal1990’s gists

kusal1990 / html.py

Created June 28, 2022 05:43


	# to open/create a new html file in the write mode
	f = open('index8051.html', 'w')

	# the html code which will go in the file GFG.html
	html_template = """<html>
	<head>
	<title></title>
	</head>
	<body>

kusal1990 / app.py

Last active June 28, 2022 05:41

	from flask import Flask, jsonify, request


	# In[ ]:


	import flask
	app = Flask(__name__)

kusal1990 / T5_model.py

Created June 25, 2022 07:41

	from transformers import T5ForConditionalGeneration, AdamW
	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
	model = T5ForConditionalGeneration.from_pretrained("allenai/unifiedqa-t5-base")

	model.cuda()

	from transformers import get_linear_schedule_with_warmup
	# Parameters:
	lr = 1e-4
	max_grad_norm = 1.0

kusal1990 / T5_token.py

Created June 25, 2022 07:30

	from transformers import AutoTokenizer, T5ForConditionalGeneration

	tokenizer = AutoTokenizer.from_pretrained('t5-small')
	def arc_preprocessor(dataset, tokenizer):
	'''
	This function will convert a given context, question, choices in a format:

	input: question \n options \n context </s>
	target: label </s>

kusal1990 / XLNET_MODEL.py

Created June 25, 2022 07:09

	from transformers import TFXLNetForMultipleChoice
	easy_train_dict = {'input_tokens':easy_train_input_ids,
	'attention_mask':easy_train_attention_mask}

	viola = tf.data.Dataset.from_tensor_slices((easy_train_dict,tf.keras.utils.to_categorical(easy_train_labels.values)))
	viola = viola.shuffle(32).batch(8).cache().prefetch(tf.data.experimental.AUTOTUNE)
	easy_dev_dict = {'input_tokens':easy_dev_input_ids,
	'attention_mask':easy_dev_attention_mask}

	viola_dev = tf.data.Dataset.from_tensor_slices((easy_dev_dict,tf.keras.utils.to_categorical(easy_dev_labels.values, num_classes=5)))

kusal1990 / XLNet_Tokenization.py

Created June 25, 2022 07:04

	from transformers import AutoTokenizer
	tokenizer = AutoTokenizer.from_pretrained('xlnet-base-cased', do_lower_case=True)

	def arc_preprocessor(dataset, tokenizer):
	'''
	This function will convert a given article, question, choices in a format:

	article <sep> question choices[0] <sep> <cls>
	article <sep> question choices[1] <sep> <cls>
	article <sep> question choices[2] <sep> <cls>

kusal1990 / Roberta_model.py

Created June 25, 2022 06:51

	pre_trained_model = TFRobertaForMultipleChoice.from_pretrained('roberta-base')

	model_input_ids = Input(shape=(5,128,), name='input_tokens', dtype='int32')
	masks_input = Input(shape=(5,128,), name='attention_mask', dtype='int32')

	x = {'input_ids':model_input_ids,
	'attention_mask':masks_input}

	x = pre_trained_model(x)['logits']
	outputs = Dense(5, activation='softmax')(x)

kusal1990 / roberta_tokenization.py

Created June 25, 2022 06:47

	from transformers import RobertaTokenizer, TFRobertaForMultipleChoice
	tokenizer = RobertaTokenizer.from_pretrained('roberta-base', do_lower_case=True)
	def arc_preprocessor(dataset, tokenizer):
	'''
	This function will convert a given article, question, choices in a format:

	<s> article </s> </s> question </s> </s> choices[0] </s>
	<s> article </s> </s> question </s> </s> choices[1] </s>
	<s> article </s> </s> question </s> </s> choices[2] </s>
	<s> article </s> </s> question </s> </s> choices[3] </s>

kusal1990 / BERT_MODEL.py

Created June 25, 2022 06:36

	tf.keras.backend.clear_session()
	pre_trained_model = TFBertForMultipleChoice.from_pretrained('bert-base-uncased')

	model_input_ids = Input(shape=(5,512,), name='input_tokens', dtype='int32')
	masks_input = Input(shape=(5,512,), name='attention_mask', dtype='int32')
	model_token_type_ids = Input(shape=(5,512,), name='token_type_ids', dtype='int32')

	x = {'input_ids':model_input_ids,
	'attention_mask':masks_input,
	'token_type_ids':model_token_type_ids}

kusal1990 / bert_model_ tokenizer.py

Created June 25, 2022 06:31

	from transformers import BertTokenizer, TFBertForMultipleChoice
	tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)

	def arc_preprocessor(dataset, tokenizer):
	'''
	This function will convert a given context, question, choices in a format:

	[CLS] context [SEP] question choices[0] [SEP]
	[CLS] context [SEP] question choices[1] [SEP]
	[CLS] context [SEP] question choices[2] [SEP]

KUSAL BERA kusal1990