Install the Transformers, Datasets, and Evaluate libraries to run this notebook.
!pip install datasets evaluate transformers[sentencepiece]
!apt install git-lfs
You will need to setup git, adapt your email and name in the following cell.
!git config --global user.email "you@example.com"
!git config --global user.name "Your Name"
You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials.
from huggingface_hub import notebook_login
notebook_login()
from transformers import TFAutoModelForMaskedLM
model_checkpoint = "distilbert-base-uncased"
model = TFAutoModelForMaskedLM.from_pretrained(model_checkpoint)
model.summary()
Model: "tf_distil_bert_for_masked_lm" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= distilbert (TFDistilBertMain multiple 66362880 _________________________________________________________________ vocab_transform (Dense) multiple 590592 _________________________________________________________________ vocab_layer_norm (LayerNorma multiple 1536 _________________________________________________________________ vocab_projector (TFDistilBer multiple 23866170 ================================================================= Total params: 66,985,530 Trainable params: 66,985,530 Non-trainable params: 0 _________________________________________________________________
text = "This is a great [MASK]."
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
import numpy as np
import tensorflow as tf
inputs = tokenizer(text, return_tensors="np")
token_logits = model(**inputs).logits
# Find the location of [MASK] and extract its logits
mask_token_index = np.argwhere(inputs["input_ids"] == tokenizer.mask_token_id)[0, 1]
mask_token_logits = token_logits[0, mask_token_index, :]
# Pick the [MASK] candidates with the highest logits
# We negate the array before argsort to get the largest, not the smallest, logits
top_5_tokens = np.argsort(-mask_token_logits)[:5].tolist()
for token in top_5_tokens:
print(f">>> {text.replace(tokenizer.mask_token, tokenizer.decode([token]))}")
'>>> This is a great deal.' '>>> This is a great success.' '>>> This is a great adventure.' '>>> This is a great idea.' '>>> This is a great feat.'
from datasets import load_dataset
imdb_dataset = load_dataset("imdb")
imdb_dataset
DatasetDict({
train: Dataset({
features: ['text', 'label'],
num_rows: 25000
})
test: Dataset({
features: ['text', 'label'],
num_rows: 25000
})
unsupervised: Dataset({
features: ['text', 'label'],
num_rows: 50000
})
})
sample = imdb_dataset["train"].shuffle(seed=42).select(range(3))
for row in sample:
print(f"\n'>>> Review: {row['text']}'")
print(f"'>>> Label: {row['label']}'")
'>>> Review: This is your typical Priyadarshan movie--a bunch of loony characters out on some silly mission. His signature climax has the entire cast of the film coming together and fighting each other in some crazy moshpit over hidden money. Whether it is a winning lottery ticket in Malamaal Weekly, black money in Hera Pheri, "kodokoo" in Phir Hera Pheri, etc., etc., the director is becoming ridiculously predictable. Don\'t get me wrong; as clichéd and preposterous his movies may be, I usually end up enjoying the comedy. However, in most his previous movies there has actually been some good humor, (Hungama and Hera Pheri being noteworthy ones). Now, the hilarity of his films is fading as he is using the same formula over and over again.<br /><br />Songs are good. Tanushree Datta looks awesome. Rajpal Yadav is irritating, and Tusshar is not a whole lot better. Kunal Khemu is OK, and Sharman Joshi is the best.' '>>> Label: 0' '>>> Review: Okay, the story makes no sense, the characters lack any dimensionally, the best dialogue is ad-libs about the low quality of movie, the cinematography is dismal, and only editing saves a bit of the muddle, but Sam" Peckinpah directed the film. Somehow, his direction is not enough. For those who appreciate Peckinpah and his great work, this movie is a disappointment. Even a great cast cannot redeem the time the viewer wastes with this minimal effort.<br /><br />The proper response to the movie is the contempt that the director San Peckinpah, James Caan, Robert Duvall, Burt Young, Bo Hopkins, Arthur Hill, and even Gig Young bring to their work. Watch the great Peckinpah films. Skip this mess.' '>>> Label: 0' '>>> Review: I saw this movie at the theaters when I was about 6 or 7 years old. I loved it then, and have recently come to own a VHS version. <br /><br />My 4 and 6 year old children love this movie and have been asking again and again to watch it. <br /><br />I have enjoyed watching it again too. Though I have to admit it is not as good on a little TV.<br /><br />I do not have older children so I do not know what they would think of it. <br /><br />The songs are very cute. My daughter keeps singing them over and over.<br /><br />Hope this helps.' '>>> Label: 1'
def tokenize_function(examples):
result = tokenizer(examples["text"])
if tokenizer.is_fast:
result["word_ids"] = [result.word_ids(i) for i in range(len(result["input_ids"]))]
return result
# Use batched=True to activate fast multithreading!
tokenized_datasets = imdb_dataset.map(
tokenize_function, batched=True, remove_columns=["text", "label"]
)
tokenized_datasets
DatasetDict({
train: Dataset({
features: ['attention_mask', 'input_ids', 'word_ids'],
num_rows: 25000
})
test: Dataset({
features: ['attention_mask', 'input_ids', 'word_ids'],
num_rows: 25000
})
unsupervised: Dataset({
features: ['attention_mask', 'input_ids', 'word_ids'],
num_rows: 50000
})
})
tokenizer.model_max_length
512
chunk_size = 128
# Slicing produces a list of lists for each feature
tokenized_samples = tokenized_datasets["train"][:3]
for idx, sample in enumerate(tokenized_samples["input_ids"]):
print(f"'>>> Review {idx} length: {len(sample)}'")
'>>> Review 0 length: 200' '>>> Review 1 length: 559' '>>> Review 2 length: 192'
concatenated_examples = {
k: sum(tokenized_samples[k], []) for k in tokenized_samples.keys()
}
total_length = len(concatenated_examples["input_ids"])
print(f"'>>> Concatenated reviews length: {total_length}'")
'>>> Concatenated reviews length: 951'
chunks = {
k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]
for k, t in concatenated_examples.items()
}
for chunk in chunks["input_ids"]:
print(f"'>>> Chunk length: {len(chunk)}'")
'>>> Chunk length: 128' '>>> Chunk length: 128' '>>> Chunk length: 128' '>>> Chunk length: 128' '>>> Chunk length: 128' '>>> Chunk length: 128' '>>> Chunk length: 128' '>>> Chunk length: 55'
def group_texts(examples):
# Concatenate all texts
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
# Compute length of concatenated texts
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the last chunk if it's smaller than chunk_size
total_length = (total_length // chunk_size) * chunk_size
# Split by chunks of max_len
result = {
k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]
for k, t in concatenated_examples.items()
}
# Create a new labels column
result["labels"] = result["input_ids"].copy()
return result
lm_datasets = tokenized_datasets.map(group_texts, batched=True)
lm_datasets
DatasetDict({
train: Dataset({
features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],
num_rows: 61289
})
test: Dataset({
features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],
num_rows: 59905
})
unsupervised: Dataset({
features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],
num_rows: 122963
})
})
tokenizer.decode(lm_datasets["train"][1]["input_ids"])
".... at.......... high. a classic line : inspector : i'm here to sack one of your teachers. student : welcome to bromwell high. i expect that many adults of my age think that bromwell high is far fetched. what a pity that it isn't! [SEP] [CLS] homelessness ( or houselessness as george carlin stated ) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. most people think of the homeless"
from transformers import DataCollatorForLanguageModeling
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)
samples = [lm_datasets["train"][i] for i in range(2)]
for sample in samples:
_ = sample.pop("word_ids")
for chunk in data_collator(samples)["input_ids"]:
print(f"\n'>>> {tokenizer.decode(chunk)}'")
import collections
import numpy as np
from transformers.data.data_collator import tf_default_data_collator
wwm_probability = 0.2
def whole_word_masking_data_collator(features):
for feature in features:
word_ids = feature.pop("word_ids")
# Create a map between words and corresponding token indices
mapping = collections.defaultdict(list)
current_word_index = -1
current_word = None
for idx, word_id in enumerate(word_ids):
if word_id is not None:
if word_id != current_word:
current_word = word_id
current_word_index += 1
mapping[current_word_index].append(idx)
# Randomly mask words
mask = np.random.binomial(1, wwm_probability, (len(mapping),))
input_ids = feature["input_ids"]
labels = feature["labels"]
new_labels = [-100] * len(labels)
for word_id in np.where(mask)[0]:
word_id = word_id.item()
for idx in mapping[word_id]:
new_labels[idx] = labels[idx]
input_ids[idx] = tokenizer.mask_token_id
feature["labels"] = new_labels
return tf_default_data_collator(features)
samples = [lm_datasets["train"][i] for i in range(2)]
batch = whole_word_masking_data_collator(samples)
for chunk in batch["input_ids"]:
print(f"\n'>>> {tokenizer.decode(chunk)}'")
'>>> [CLS] bromwell high is a cartoon comedy [MASK] it ran at the same time as some other programs about school life, such as " teachers ". my 35 years in the teaching profession lead me to believe that bromwell high\'s satire is much closer to reality than is " teachers ". the scramble to survive financially, the insightful students who can see right through their pathetic teachers\'pomp, the pettiness of the whole situation, all remind me of the schools i knew and their students. when i saw the episode in which a student repeatedly tried to burn down the school, i immediately recalled.....' '>>> .... [MASK] [MASK] [MASK] [MASK]....... high. a classic line : inspector : i\'m here to sack one of your teachers. student : welcome to bromwell high. i expect that many adults of my age think that bromwell high is far fetched. what a pity that it isn\'t! [SEP] [CLS] homelessness ( or houselessness as george carlin stated ) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. most people think of the homeless'
train_size = 10_000
test_size = int(0.1 * train_size)
downsampled_dataset = lm_datasets["train"].train_test_split(
train_size=train_size, test_size=test_size, seed=42
)
downsampled_dataset
DatasetDict({
train: Dataset({
features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],
num_rows: 10000
})
test: Dataset({
features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],
num_rows: 1000
})
})
from huggingface_hub import notebook_login
notebook_login()
tf_train_dataset = model.prepare_tf_dataset(
downsampled_dataset["train"],
collate_fn=data_collator,
shuffle=True,
batch_size=32,
)
tf_eval_dataset = model.prepare_tf_dataset(
downsampled_dataset["test"],
collate_fn=data_collator,
shuffle=False,
batch_size=32,
)
from transformers import create_optimizer
from transformers.keras_callbacks import PushToHubCallback
import tensorflow as tf
num_train_steps = len(tf_train_dataset)
optimizer, schedule = create_optimizer(
init_lr=2e-5,
num_warmup_steps=1_000,
num_train_steps=num_train_steps,
weight_decay_rate=0.01,
)
model.compile(optimizer=optimizer)
# Train in mixed-precision float16
tf.keras.mixed_precision.set_global_policy("mixed_float16")
model_name = model_checkpoint.split("/")[-1]
callback = PushToHubCallback(
output_dir=f"{model_name}-finetuned-imdb", tokenizer=tokenizer
)
import math
eval_loss = model.evaluate(tf_eval_dataset)
print(f"Perplexity: {math.exp(eval_loss):.2f}")
>>> Perplexity: 21.75
model.fit(tf_train_dataset, validation_data=tf_eval_dataset, callbacks=[callback])
eval_loss = model.evaluate(tf_eval_dataset)
print(f"Perplexity: {math.exp(eval_loss):.2f}")
>>> Perplexity: 11.32
from transformers import pipeline
mask_filler = pipeline(
"fill-mask", model="huggingface-course/distilbert-base-uncased-finetuned-imdb"
)
preds = mask_filler(text)
for pred in preds:
print(f">>> {pred['sequence']}")
'>>> this is a great movie.' '>>> this is a great film.' '>>> this is a great story.' '>>> this is a great movies.' '>>> this is a great character.'