section6_tf.ipynb

Kernel: Unknown Kernel

因果言語モデルを一から学習 (TensorFlow)

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 "[email protected]"
!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()

def any_keyword_in_string(string, keywords):
    for keyword in keywords:
        if keyword in string:
            return True
    return False

filters = ["pandas", "sklearn", "matplotlib", "seaborn"]
example_1 = "import numpy as np"
example_2 = "import pandas as pd"

print(
    any_keyword_in_string(example_1, filters), any_keyword_in_string(example_2, filters)
)

False True

from collections import defaultdict
from tqdm import tqdm
from datasets import Dataset


def filter_streaming_dataset(dataset, filters):
    filtered_dict = defaultdict(list)
    total = 0
    for sample in tqdm(iter(dataset)):
        total += 1
        if any_keyword_in_string(sample["content"], filters):
            for k, v in sample.items():
                filtered_dict[k].append(v)
    print(f"{len(filtered_dict['content'])/total:.2%} of data after filtering.")
    return Dataset.from_dict(filtered_dict)

# This cell will take a very long time to execute, so you should skip it and go to
# the next one!
from datasets import load_dataset

split = "train"  # "valid"
filters = ["pandas", "sklearn", "matplotlib", "seaborn"]

data = load_dataset(f"transformersbook/codeparrot-{split}", split=split, streaming=True)
filtered_data = filter_streaming_dataset(data, filters)

3.26% of data after filtering.

from datasets import load_dataset, DatasetDict

ds_train = load_dataset("huggingface-course/codeparrot-ds-train", split="train")
ds_valid = load_dataset("huggingface-course/codeparrot-ds-valid", split="validation")

raw_datasets = DatasetDict(
    {
        "train": ds_train,  # .shuffle().select(range(50000)),
        "valid": ds_valid,  # .shuffle().select(range(500))
    }
)

raw_datasets

DatasetDict({
    train: Dataset({
        features: ['repo_name', 'path', 'copies', 'size', 'content', 'license'],
        num_rows: 606720
    })
    valid: Dataset({
        features: ['repo_name', 'path', 'copies', 'size', 'content', 'license'],
        num_rows: 3322
    })
})

for key in raw_datasets["train"][0]:
    print(f"{key.upper()}: {raw_datasets['train'][0][key][:200]}")

'REPO_NAME: kmike/scikit-learn'
'PATH: sklearn/utils/__init__.py'
'COPIES: 3'
'SIZE: 10094'
'''CONTENT: """
The :mod:`sklearn.utils` module includes various utilites.
"""

from collections import Sequence

import numpy as np
from scipy.sparse import issparse
import warnings

from .murmurhash import murm
LICENSE: bsd-3-clause'''

from transformers import AutoTokenizer

context_length = 128
tokenizer = AutoTokenizer.from_pretrained("huggingface-course/code-search-net-tokenizer")

outputs = tokenizer(
    raw_datasets["train"][:2]["content"],
    truncation=True,
    max_length=context_length,
    return_overflowing_tokens=True,
    return_length=True,
)

print(f"Input IDs length: {len(outputs['input_ids'])}")
print(f"Input chunk lengths: {(outputs['length'])}")
print(f"Chunk mapping: {outputs['overflow_to_sample_mapping']}")

Input IDs length: 34
Input chunk lengths: [128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 117, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 41]
Chunk mapping: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

def tokenize(element):
    outputs = tokenizer(
        element["content"],
        truncation=True,
        max_length=context_length,
        return_overflowing_tokens=True,
        return_length=True,
    )
    input_batch = []
    for length, input_ids in zip(outputs["length"], outputs["input_ids"]):
        if length == context_length:
            input_batch.append(input_ids)
    return {"input_ids": input_batch}


tokenized_datasets = raw_datasets.map(
    tokenize, batched=True, remove_columns=raw_datasets["train"].column_names
)
tokenized_datasets

DatasetDict({
    train: Dataset({
        features: ['input_ids'],
        num_rows: 16702061
    })
    valid: Dataset({
        features: ['input_ids'],
        num_rows: 93164
    })
})

from transformers import AutoTokenizer, TFGPT2LMHeadModel, AutoConfig

config = AutoConfig.from_pretrained(
    "gpt2",
    vocab_size=len(tokenizer),
    n_ctx=context_length,
    bos_token_id=tokenizer.bos_token_id,
    eos_token_id=tokenizer.eos_token_id,
)

model = TFGPT2LMHeadModel(config)
model(model.dummy_inputs)  # Builds the model
model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
transformer (TFGPT2MainLayer multiple                  124242432 
=================================================================
Total params: 124,242,432
Trainable params: 124,242,432
Non-trainable params: 0
_________________________________________________________________

from transformers import DataCollatorForLanguageModeling

tokenizer.pad_token = tokenizer.eos_token
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False, return_tensors="tf")

out = data_collator([tokenized_datasets["train"][i] for i in range(5)])
for key in out:
    print(f"{key} shape: {out[key].shape}")

input_ids shape: (5, 128)
attention_mask shape: (5, 128)
labels shape: (5, 128)

tf_train_dataset = tokenized_dataset["train"].to_tf_dataset(
    columns=["input_ids", "attention_mask", "labels"],
    collate_fn=data_collator,
    shuffle=True,
    batch_size=32,
)
tf_eval_dataset = tokenized_dataset["valid"].to_tf_dataset(
    columns=["input_ids", "attention_mask", "labels"],
    collate_fn=data_collator,
    shuffle=False,
    batch_size=32,
)

from huggingface_hub import notebook_login

notebook_login()

from transformers import create_optimizer
import tensorflow as tf

num_train_steps = len(tf_train_dataset)
optimizer, schedule = create_optimizer(
    init_lr=5e-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")

from transformers.keras_callbacks import PushToHubCallback

callback = PushToHubCallback(output_dir="codeparrot-ds", tokenizer=tokenizer)

model.fit(tf_train_dataset, validation_data=tf_eval_dataset, callbacks=[callback])

from transformers import pipeline

course_model = TFGPT2LMHeadModel.from_pretrained("huggingface-course/codeparrot-ds")
course_tokenizer = AutoTokenizer.from_pretrained("huggingface-course/codeparrot-ds")
pipe = pipeline(
    "text-generation", model=course_model, tokenizer=course_tokenizer, device=0
)

txt = """\
# create some data
x = np.random.randn(100)
y = np.random.randn(100)

# create scatter plot with x, y
"""
print(pipe(txt, num_return_sequences=1)[0]["generated_text"])

# create some data
x = np.random.randn(100)
y = np.random.randn(100)

# create scatter plot with x, y
plt.scatter(x, y)

# create scatter

txt = """\
# create some data
x = np.random.randn(100)
y = np.random.randn(100)

# create dataframe from x and y
"""
print(pipe(txt, num_return_sequences=1)[0]["generated_text"])

# create some data
x = np.random.randn(100)
y = np.random.randn(100)

# create dataframe from x and y
df = pd.DataFrame({'x': x, 'y': y})
df.insert(0,'x', x)
for

txt = """\
# dataframe with profession, income and name
df = pd.DataFrame({'profession': x, 'income':y, 'name': z})

# calculate the mean income per profession
"""
print(pipe(txt, num_return_sequences=1)[0]["generated_text"])

# dataframe with profession, income and name
df = pd.DataFrame({'profession': x, 'income':y, 'name': z})

# calculate the mean income per profession
profession = df.groupby(['profession']).mean()

# compute the

txt = """
# import random forest regressor from scikit-learn
from sklearn.ensemble import RandomForestRegressor

# fit random forest model with 300 estimators on X, y:
"""
print(pipe(txt, num_return_sequences=1)[0]["generated_text"])

# import random forest regressor from scikit-learn
from sklearn.ensemble import RandomForestRegressor

# fit random forest model with 300 estimators on X, y:
rf = RandomForestRegressor(n_estimators=300, random_state=random_state, max_depth=3)
rf.fit(X, y)
rf