language_modeling_ipynb.py

# -*- coding: utf-8 -*-
"""“language_modeling.ipynb”的副本

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1baqtirf_2hHx2-byvSi0iZo4g_5Rm_nZ
"""

# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git

"""# Causal language modeling

There are two types of language modeling, causal and masked. This guide illustrates causal language modeling.
Causal language models are frequently used for text generation. You can use these models for creative applications like
choosing your own text adventure or an intelligent coding assistant like Copilot or CodeParrot.
"""

#@title
from IPython.display import HTML

HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/Vpjb1lu0MDk?rel=0&amp;controls=0&amp;showinfo=0" frameborder="0" allowfullscreen></iframe>')

"""Causal language modeling predicts the next token in a sequence of tokens, and the model can only attend to tokens on
the left. This means the model cannot see future tokens. GPT-2 is an example of a causal language model.

This guide will show you how to:

1. Finetune [DistilGPT2](https://huggingface.co./distilgpt2) on the [r/askscience](https://www.reddit.com/r/askscience/) subset of the [ELI5](https://huggingface.co./datasets/eli5) dataset.
2. Use your finetuned model for inference.

<Tip>
You can finetune other architectures for causal language modeling following the same steps in this guide.
Choose one of the following architectures:

<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
[BART](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/bart), [BERT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/bert), [Bert Generation](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/bert-generation), [BigBird](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/big_bird), [BigBird-Pegasus](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/bigbird_pegasus), [BioGpt](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/biogpt), [Blenderbot](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/blenderbot), [BlenderbotSmall](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/blenderbot-small), [BLOOM](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/bloom), [CamemBERT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/camembert), [CodeGen](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/codegen), [CPM-Ant](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/cpmant), [CTRL](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/ctrl), [Data2VecText](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/data2vec-text), [ELECTRA](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/electra), [ERNIE](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/ernie), [GIT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/git), [GPT-Sw3](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/gpt-sw3), [OpenAI GPT-2](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/gpt2), [GPTBigCode](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/gpt_bigcode), [GPT Neo](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/gpt_neo), [GPT NeoX](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/gpt_neox), [GPT NeoX Japanese](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/gpt_neox_japanese), [GPT-J](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/gptj), [LLaMA](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/llama), [Marian](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/marian), [mBART](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/mbart), [MEGA](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/mega), [Megatron-BERT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/megatron-bert), [MVP](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/mvp), [OpenLlama](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/open-llama), [OpenAI GPT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/openai-gpt), [OPT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/opt), [Pegasus](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/pegasus), [PLBart](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/plbart), [ProphetNet](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/prophetnet), [QDQBert](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/qdqbert), [Reformer](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/reformer), [RemBERT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/rembert), [RoBERTa](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/roberta), [RoBERTa-PreLayerNorm](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/roberta-prelayernorm), [RoCBert](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/roc_bert), [RoFormer](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/roformer), [RWKV](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/rwkv), [Speech2Text2](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/speech_to_text_2), [Transformer-XL](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/transfo-xl), [TrOCR](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/trocr), [XGLM](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/xglm), [XLM](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/xlm), [XLM-ProphetNet](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/xlm-prophetnet), [XLM-RoBERTa](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/xlm-roberta), [XLM-RoBERTa-XL](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/xlm-roberta-xl), [XLNet](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/xlnet), [X-MOD](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/xmod)


<!--End of the generated tip-->

</Tip>

Before you begin, make sure you have all the necessary libraries installed:

```bash
pip install transformers datasets evaluate
```

We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in:
"""

from huggingface_hub import notebook_login

notebook_login()

"""## Load ELI5 dataset

Start by loading a smaller subset of the r/askscience subset of the ELI5 dataset from the 🤗 Datasets library.
 This'll give you a chance to experiment and make sure everything works before spending more time training on the full dataset.
"""

from datasets import load_dataset

eli5 = load_dataset("eli5", split="train_asks[:5000]")

"""Split the dataset's `train_asks` split into a train and test set with the [train_test_split](https://huggingface.co./docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.train_test_split) method:"""

eli5 = eli5.train_test_split(test_size=0.2)

"""Then take a look at an example:"""

eli5["train"][0]

"""While this may look like a lot, you're only really interested in the `text` field. What's cool about language modeling
tasks is you don't need labels (also known as an unsupervised task) because the next word *is* the label.

## Preprocess
"""

#@title
from IPython.display import HTML

HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/ma1TrR7gE7I?rel=0&amp;controls=0&amp;showinfo=0" frameborder="0" allowfullscreen></iframe>')

"""The next step is to load a DistilGPT2 tokenizer to process the `text` subfield:"""

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("distilgpt2")

"""You'll notice from the example above, the `text` field is actually nested inside `answers`. This means you'll need to
extract the `text` subfield from its nested structure with the [`flatten`](https://huggingface.co./docs/datasets/process.html#flatten) method:
"""

eli5 = eli5.flatten()
eli5["train"][0]

"""Each subfield is now a separate column as indicated by the `answers` prefix, and the `text` field is a list now. Instead
of tokenizing each sentence separately, convert the list to a string so you can jointly tokenize them.

Here is a first preprocessing function to join the list of strings for each example and tokenize the result:
"""

def preprocess_function(examples):
    return tokenizer([" ".join(x) for x in examples["answers.text"]])

"""To apply this preprocessing function over the entire dataset, use the 🤗 Datasets [map](https://huggingface.co./docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.map) method. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once, and increasing the number of processes with `num_proc`. Remove any columns you don't need:"""

tokenized_eli5 = eli5.map(
    preprocess_function,
    batched=True,
    num_proc=4,
    remove_columns=eli5["train"].column_names,
)

"""This dataset contains the token sequences, but some of these are longer than the maximum input length for the model.

You can now use a second preprocessing function to
- concatenate all the sequences
- split the concatenated sequences into shorter chunks defined by `block_size`, which should be both shorter than the maximum input length and short enough for your GPU RAM.
"""

block_size = 128


def group_texts(examples):
    # Concatenate all texts.
    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
    total_length = len(concatenated_examples[list(examples.keys())[0]])
    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
    # customize this part to your needs.
    if total_length >= block_size:
        total_length = (total_length // block_size) * block_size
    # Split by chunks of block_size.
    result = {
        k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
        for k, t in concatenated_examples.items()
    }
    result["labels"] = result["input_ids"].copy()
    return result

"""Apply the `group_texts` function over the entire dataset:"""

lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4)

"""Now create a batch of examples using [DataCollatorForLanguageModeling](https://huggingface.co./docs/transformers/main/en/main_classes/data_collator#transformers.DataCollatorForLanguageModeling). It's more efficient to *dynamically pad* the
sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length.

Use the end-of-sequence token as the padding token and set `mlm=False`. This will use the inputs as labels shifted to the right by one element:
"""

from transformers import DataCollatorForLanguageModeling

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

"""## Train

<Tip>

If you aren't familiar with finetuning a model with the [Trainer](https://huggingface.co./docs/transformers/main/en/main_classes/trainer#transformers.Trainer), take a look at the [basic tutorial](https://huggingface.co./docs/transformers/main/en/tasks/../training#train-with-pytorch-trainer)!

</Tip>

You're ready to start training your model now! Load DistilGPT2 with [AutoModelForCausalLM](https://huggingface.co./docs/transformers/main/en/model_doc/auto#transformers.AutoModelForCausalLM):
"""

from transformers import AutoModelForCausalLM, TrainingArguments, Trainer

model = AutoModelForCausalLM.from_pretrained("distilgpt2")

"""At this point, only three steps remain:

1. Define your training hyperparameters in [TrainingArguments](https://huggingface.co./docs/transformers/main/en/main_classes/trainer#transformers.TrainingArguments). The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model).
2. Pass the training arguments to [Trainer](https://huggingface.co./docs/transformers/main/en/main_classes/trainer#transformers.Trainer) along with the model, datasets, and data collator.
3. Call [train()](https://huggingface.co./docs/transformers/main/en/main_classes/trainer#transformers.Trainer.train) to finetune your model.
"""

training_args = TrainingArguments(
    output_dir="my_awesome_eli5_clm-model",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    weight_decay=0.01,
    push_to_hub=True,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=lm_dataset["train"],
    eval_dataset=lm_dataset["test"],
    data_collator=data_collator,
)

trainer.train()

"""Once training is completed, use the [evaluate()](https://huggingface.co./docs/transformers/main/en/main_classes/trainer#transformers.Trainer.evaluate) method to evaluate your model and get its perplexity:"""

import math

eval_results = trainer.evaluate()
print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}")

"""Then share your model to the Hub with the [push_to_hub()](https://huggingface.co./docs/transformers/main/en/main_classes/trainer#transformers.Trainer.push_to_hub) method so everyone can use your model:"""

trainer.push_to_hub()

"""<Tip>

For a more in-depth example of how to finetune a model for causal language modeling, take a look at the corresponding
[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)
or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).

</Tip>

## Inference

Great, now that you've finetuned a model, you can use it for inference!

Come up with a prompt you'd like to generate text from:
"""

prompt = "Somatic hypermutation allows the immune system to"

"""The simplest way to try out your finetuned model for inference is to use it in a [pipeline()](https://huggingface.co./docs/transformers/main/en/main_classes/pipelines#transformers.pipeline). Instantiate a `pipeline` for text generation with your model, and pass your text to it:"""

from transformers import pipeline

generator = pipeline("text-generation", model="my_awesome_eli5_clm-model")
generator(prompt)

"""Tokenize the text and return the `input_ids` as PyTorch tensors:"""

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("my_awesome_eli5_clm-model")
inputs = tokenizer(prompt, return_tensors="pt").input_ids

"""Use the [generate()](https://huggingface.co./docs/transformers/main/en/main_classes/text_generation#transformers.GenerationMixin.generate) method to generate text.
For more details about the different text generation strategies and parameters for controlling generation, check out the [Text generation strategies](https://huggingface.co./docs/transformers/main/en/tasks/../generation_strategies) page.
"""

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("my_awesome_eli5_clm-model")
outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)

"""Decode the generated token ids back into text:"""

tokenizer.batch_decode(outputs, skip_special_tokens=True)