How to efficiently convert a large parallel corpus to a Huggingface dataset to train an EncoderDecoderModel?

A parallel corpus is a collection of texts in two or more languages that are aligned or translated versions of each other. For example, a parallel corpus of English and French sentences can be used to train a machine translation model that can translate from one language to another.

A Huggingface dataset is a standardized and lightweight way of handling and processing data for natural language processing (NLP) tasks. It provides various features such as caching, streaming, filtering, shuffling, and splitting of data. A Huggingface dataset can be created from various sources, such as local files, online files, pandas dataframes, or in-memory data.

An EncoderDecoderModel is a type of model that consists of two sub-models: an encoder and a decoder. The encoder takes an input sequence and encodes it into a hidden representation. The decoder takes the hidden representation and generates an output sequence. An EncoderDecoderModel can be used for various NLP tasks that involve generating sequences, such as machine translation, text summarization, or text generation.

To efficiently convert a large parallel corpus to a Huggingface dataset to train an EncoderDecoderModel, you can follow these steps:

Step 1: Load the parallel corpus from local files or online files

You can use the load_dataset function from the datasets library to load the parallel corpus from local files or online files. You need to specify the format of the files, such as csv, json, or txt, and the column names or fields that contain the source and target texts. For example, if you have a parallel corpus of English and French sentences in two csv files, you can load them as follows:

from datasets import load_dataset

dataset = load_dataset(
    "csv",
    data_files={
        "en": "path/to/english.csv",
        "fr": "path/to/french.csv",
    },
    column_names=["text"],
)

Step 2: Preprocess the source and target texts

You can use the map method of the dataset object to apply some preprocessing functions to the source and target texts. For example, you can tokenize the texts using a tokenizer from the transformers library, such as BertTokenizer or MarianTokenizer. You can also truncate or pad the texts to a fixed length, and add special tokens such as <s> and </s> to mark the start and end of the sequences. For example, if you want to use a MarianTokenizer for English and French, you can preprocess the texts as follows:

from transformers import MarianTokenizer

tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-fr")

def preprocess_function(examples):
    inputs = [ex["text"] for ex in examples["en"]]
    outputs = [ex["text"] for ex in examples["fr"]]
    inputs = tokenizer(inputs, max_length=128, truncation=True, padding="max_length")
    outputs = tokenizer(outputs, max_length=128, truncation=True, padding="max_length")
    inputs["input_ids"] = [[tokenizer.bos_token_id] + ids + [tokenizer.eos_token_id] for ids in inputs["input_ids"]]
    outputs["input_ids"] = [[tokenizer.bos_token_id] + ids + [tokenizer.eos_token_id] for ids in outputs["input_ids"]]
    return {"input_ids": inputs["input_ids"], "attention_mask": inputs["attention_mask"], "decoder_input_ids": outputs["input_ids"], "decoder_attention_mask": outputs["attention_mask"]}

dataset = dataset.map(preprocess_function, batched=True)

Step 3: Split the dataset into train, validation, and test sets

You can use the train_test_split method of the dataset object to split the dataset into train, validation, and test sets. You need to specify the ratio or size of each set, and optionally a random seed for reproducibility. For example, if you want to split the dataset into 80% train, 10% validation, and 10% test sets, you can do as follows:

dataset = dataset.train_test_split(test_size=0.1, seed=42)
test_dataset = dataset["test"]

dataset = dataset["train"].train_test_split(test_size=0.1111, seed=42)
train_dataset = dataset["train"]
val_dataset = dataset["test"]

Step 4: Train the EncoderDecoderModel using the Trainer class from the transformers library

You can use the Trainer class from the transformers library to train the EncoderDecoderModel using the Huggingface dataset. You need to specify the model, the training arguments, the data collator, the training and validation datasets, and optionally the evaluation metrics. For example, if you want to train a MarianMTModel for English to French translation, you can do as follows:

from transformers import MarianMTModel, Seq2SeqTrainingArguments, Seq2SeqTrainer, DataCollatorForSeq2Seq, MarianConfig

model = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-fr")

training_args = Seq2SeqTrainingArguments(
    output_dir="path/to/output",
    num_train_epochs=3,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    logging_steps=100,
    save_steps=500,
    evaluation_strategy="steps",
    predict_with_generate=True,
)

data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)

trainer = Seq2SeqTrainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
)

trainer.train()

Step 5: Evaluate the EncoderDecoderModel on the test set using the Trainer class

You can use the evaluate method of the trainer object to evaluate the EncoderDecoderModel on the test set. You can also use the predict method to generate predictions on the test set. You can use the compute_metrics function to define the evaluation metrics, such as BLEU, ROUGE, or METEOR. For example, if you want to evaluate the MarianMTModel on the test set using BLEU, you can do as follows:

from datasets import load_metric

metric = load_metric("sacrebleu")

def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = [tokenizer.decode(pred, skip_special_tokens=True) for pred in predictions]
    labels = [tokenizer.decode(label, skip_special_tokens=True) for label in labels]
    return metric.compute(predictions=predictions, references=labels)

trainer.compute_metrics = compute_metrics

test_results = trainer.evaluate(test_dataset)
test_predictions = trainer.predict(test_dataset)