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# -*- coding: utf-8 -*-
"""Fine Tuning Number One.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1ICULTdmxijXHisMebXX5KmPzxzfZ2TtH
"""
!pip install datasets
!pip install torch
!pip install -q -U transformers accelerate
!pip install transformers[torch]
!pip install accelerate -U
!pip install huggingface_hub
from transformers import AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling, TrainingArguments, Trainer
from datasets import load_dataset
# Load the dataset
datasetTrain = load_dataset("rcds/wikipedia-for-mask-filling", "original_512", trust_remote_code=True)
datasetTest = load_dataset("rcds/wikipedia-for-mask-filling", "original_4096", trust_remote_code=True)
# Load the pre-trained model and tokenizer
tokenizerOne = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
tokenizerTwo = AutoTokenizer.from_pretrained("distilbert/distilbert-base-cased")
# Tokenize the dataset
def tokenize_function_one(examples):
return tokenizerOne(examples["texts"], padding="max_length", truncation=True)
def tokenize_function_two(examples):
return tokenizerTwo(examples["texts"], padding="max_length", truncation=True, max_length=512)
# Make the datasets
tokenized_datasets_oneTrain = datasetTrain.map(tokenize_function_one, batched=True)
tokenized_datasets_oneTest = datasetTest.map(tokenize_function_one, batched=True)
tokenized_datasets_oneTrain = tokenized_datasets_oneTrain["train"].select(range(10000))
tokenized_datasets_oneTest = tokenized_datasets_oneTest["train"].select(range(2500))
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizerOne, mlm_probability=0.15)
training_args = TrainingArguments(
"test_trainer",
num_train_epochs=3,
per_device_train_batch_size=32,
per_device_eval_batch_size=32,
warmup_steps=500,
weight_decay=0.01,
)
# Model One: google-bert/bert-base-cased
model_one = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased")
trainer_one = Trainer(
model=model_one,
args=training_args,
train_dataset=tokenized_datasets_oneTrain,
eval_dataset=tokenized_datasets_oneTest,
data_collator=data_collator,
)
trainer_one.train()
# Get your API token from HuggingFace.
api_token = "redacted"
from transformers import BertConfig, BertModel
model_one.push_to_hub("emma7897/bert_one", token = api_token)
tokenizerOne.push_to_hub("emma7897/bert_one", token = api_token)
# Make the datasets
tokenized_datasets_twoTrain = datasetTrain.map(tokenize_function_two, batched=True)
tokenized_datasets_twoTest = datasetTest.map(tokenize_function_two, batched=True)
tokenized_datasets_twoTrain = tokenized_datasets_twoTrain["train"].select(range(10000))
tokenized_datasets_twoTest = tokenized_datasets_twoTest["train"].select(range(2500))
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizerTwo, mlm_probability=0.15)
training_args = TrainingArguments(
"test_trainer",
num_train_epochs=3,
per_device_train_batch_size=48,
per_device_eval_batch_size=48,
warmup_steps=500,
weight_decay=0.01,
)
# Model Two: distilbert/distilbert-base-cased
model_two = AutoModelForMaskedLM.from_pretrained("distilbert/distilbert-base-cased")
trainer_two = Trainer(
model=model_two,
args=training_args,
train_dataset=tokenized_datasets_twoTrain,
eval_dataset=tokenized_datasets_twoTest,
data_collator=data_collator,
)
trainer_two.train()
from transformers import DistilBertConfig, DistilBertModel
# Push my DistilBert model to the Hub.
model_two.push_to_hub("emma7897/distilbert_one", token=api_token)
tokenizerTwo.push_to_hub("emma7897/distilbert_one", token=api_token) |