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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Machine-Translation-with-Huggingface-Transformer" data-toc-modified-id="Machine-Translation-with-Huggingface-Transformer-1"><span class="toc-item-num">1&nbsp;&nbsp;</span>Machine Translation with Huggingface Transformer</a></span><ul class="toc-item"><li><span><a href="#Data-Preprocessing" data-toc-modified-id="Data-Preprocessing-1.1"><span class="toc-item-num">1.1&nbsp;&nbsp;</span>Data Preprocessing</a></span></li><li><span><a href="#Pretrained-Model" data-toc-modified-id="Pretrained-Model-1.2"><span class="toc-item-num">1.2&nbsp;&nbsp;</span>Pretrained Model</a></span></li><li><span><a href="#Training-Model-From-Scratch" data-toc-modified-id="Training-Model-From-Scratch-1.3"><span class="toc-item-num">1.3&nbsp;&nbsp;</span>Training Model From Scratch</a></span></li><li><span><a href="#Training-Tokenizer-and-Model-From-Scratch" data-toc-modified-id="Training-Tokenizer-and-Model-From-Scratch-1.4"><span class="toc-item-num">1.4&nbsp;&nbsp;</span>Training Tokenizer and Model From Scratch</a></span></li></ul></li><li><span><a href="#Reference" data-toc-modified-id="Reference-2"><span class="toc-item-num">2&nbsp;&nbsp;</span>Reference</a></span></li></ul></div>
Step4:  Machine Translation with Huggingface Transformer
In this article we'll be leveraging Huggingface's Transformer on our machine translation task. The library provides thousands of pretrained models that we can use on our tasks. Apart from that, we'll also take a look at how to use its pre-built tokenizer and model architecture to train a model from scratch.
Data Preprocessing
We'll be using the Multi30k dataset to demonstrate using the transfomer model in a machine translation task. This German to English training dataset's size is around 29K, a moderate sized dataset so that we can get our results without waiting too long. We'll start off by downloading the raw dataset and extracting them. Feel free to swap this step with any other machine translation dataset.
Step5:  We print out the content in the data directory and some sample data.
Step7:  The original dataset is splits the source and the target language into two separate files (e.g. train.de, train.en are the training dataset for German and English). This type of format is useful when we wish to train a tokenizer on top of the source or target language as we'll soon see.
On the other hand, having the source and target pair together in one single file makes it easier to load them in batches for training or evaluating our machine translation model. We'll create the paired dataset, and load the dataset. For loading the dataset, it will be helpful to have some basic understanding of Huggingface's dataset.
Step8:  We can acsess the split, and each record/pair with the following syntax.
Step9:  Pretrained Model
To get started we'll use the MarianMT pretrained model translation model.
First thing we'll do is to load the pre-trained tokenizer, using the from_pretrained syntax. This ensures we get the tokenizer and vocabulary corresponding to the model architecture for this specific checkpoint.
Step10:  We can pass a single record, or a list of records to huggingface's tokenizer. Then depending on the model, we might see different keys in the dictionary returned. For example, here, we have
Step12:  We can apply the tokenizers to our entire raw dataset, so this preprocessing will be a one time process. By passing the function to our dataset dict's map method, it will apply the same tokenizing step to all the splits in our data.
Step13:  Having prepared our dataset, we'll load the pre-trained model. Similar to the tokenizer, we can use the .from_pretrained method, and specify a valid huggingface model.
Step15:  We can directly use this model to generate the translations, and eyeball the results.
Step16:  Training Model From Scratch
The next section shows the steps for training the model parameters from scratch. Instead of directly instantiating the model using .from_pretrained method. We use the .from_config method, where we specify the configurations for a particular model architecture. The configuration will be created using .from_pretrained, as well as updating some of the configuration hyper parameters, where we opted for a smaller model for faster iteration.
Step17:  The huggingface library offers pre-built functionality to avoid writing the training logic from scratch. This step can be swapped out with other higher level trainer packages or even implementing our own logic. We setup the
Step18:  We can take a look at the batched examples. Understanding the output can be beneficial if we wish to customize the data collate function later.
attention_mask Padded tokens will be masked out with 0..
input_ids. Input ids are padded with the padding special tokens.
labels. By default -100 will be automatically ignored by PyTorch loss functions, hence it will the id to use when padding the labels.
Step20:  Similar to what we did before, we can use this model to generate the translations, and eyeball the results.
Step21:  Training Tokenizer and Model From Scratch
From our raw pair, we need to use or train a tokenizer to convert them into numerical indices. Here we'll be training our tokenizer from scratch using Huggingface's tokenizer. Feel free to swap this step out with other tokenization procedures, what's important is to leave rooms for special tokens such as the init token that represents the beginning of a sentence, the end of sentence token that represents the end of a sentence, unknown token, and padding token that pads sentence batches into equivalent length.
Step22:  We'll perform this tokenization step for all our dataset up front, so we can do as little preprocessing as possible while feeding our dataset to model. Note that we do not perform the padding step at this stage.
Step24:  Given the custom tokenizer, we can also custom our data collate class that does the padding for input and labels.
Step25:  Given that we are using our own tokenizer instead of the pre-trained ones, we need to update a couple of other parameters in our config. The one that's worth pointing out is that this model starts generating with pad_token_id, that's why the decoder_start_token_id is the same as the pad_token_id.
Then rest of model training code should be the same as the ones in the previous section.
Step26:  Confirming saving and loading the model gives us identical predictions.
Step27:  As the last step, we'll write a inferencing function that performs batch scoring on a given dataset. Here we generate the predictions and save it in a pandas dataframe along with the source and the target.
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Python Code:
# code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(css_style='custom2.css', plot_style=False)
os.chdir(path)
# 1. magic for inline plot
# 2. magic to print version
# 3. magic so that the notebook will reload external python modules
# 4. magic to enable retina (high resolution) plots
# https://gist.github.com/minrk/3301035
%matplotlib inline
%load_ext watermark
%load_ext autoreload
%autoreload 2
%config InlineBackend.figure_format='retina'
import os
import math
import time
import torch
import random
import numpy as np
import pandas as pd
from datasets import load_dataset
from torch.utils.data import DataLoader
from tokenizers import ByteLevelBPETokenizer
%watermark -a 'Ethen' -d -t -v -p datasets,numpy,torch,tokenizers,transformers
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Machine-Translation-with-Huggingface-Transformer" data-toc-modified-id="Machine-Translation-with-Huggingface-Transformer-1"><span class="toc-item-num">1&nbsp;&nbsp;</span>Machine Translation with Huggingface Transformer</a></span><ul class="toc-item"><li><span><a href="#Data-Preprocessing" data-toc-modified-id="Data-Preprocessing-1.1"><span class="toc-item-num">1.1&nbsp;&nbsp;</span>Data Preprocessing</a></span></li><li><span><a href="#Pretrained-Model" data-toc-modified-id="Pretrained-Model-1.2"><span class="toc-item-num">1.2&nbsp;&nbsp;</span>Pretrained Model</a></span></li><li><span><a href="#Training-Model-From-Scratch" data-toc-modified-id="Training-Model-From-Scratch-1.3"><span class="toc-item-num">1.3&nbsp;&nbsp;</span>Training Model From Scratch</a></span></li><li><span><a href="#Training-Tokenizer-and-Model-From-Scratch" data-toc-modified-id="Training-Tokenizer-and-Model-From-Scratch-1.4"><span class="toc-item-num">1.4&nbsp;&nbsp;</span>Training Tokenizer and Model From Scratch</a></span></li></ul></li><li><span><a href="#Reference" data-toc-modified-id="Reference-2"><span class="toc-item-num">2&nbsp;&nbsp;</span>Reference</a></span></li></ul></div>
End of explanation
import tarfile
import zipfile
import requests
import subprocess
from tqdm import tqdm
from urllib.parse import urlparse
def download_file(url: str, directory: str):
    
    Download the file at ``url`` to ``directory``.
    Extract to the file content ``directory`` if the original file
    is a tar, tar.gz or zip file.
    Parameters
    ----------
    url : str
        url of the file.
    directory : str
        Directory to download the file.
    
    response = requests.get(url, stream=True)
    response.raise_for_status()
    content_len = response.headers.get('Content-Length')
    total = int(content_len) if content_len is not None else 0
    os.makedirs(directory, exist_ok=True)
    file_name = get_file_name_from_url(url)
    file_path = os.path.join(directory, file_name)
    with tqdm(unit='B', total=total) as pbar, open(file_path, 'wb') as f:
        for chunk in response.iter_content(chunk_size=1024):
            if chunk:
                pbar.update(len(chunk))
                f.write(chunk)
    extract_compressed_file(file_path, directory)
def extract_compressed_file(compressed_file_path: str, directory: str):
    
    Extract a compressed file to ``directory``. Supports zip, tar.gz, tgz,
    tar extensions.
    Parameters
    ----------
    compressed_file_path : str
    directory : str
        File will to extracted to this directory.
    
    basename = os.path.basename(compressed_file_path)
    if 'zip' in basename:
        with zipfile.ZipFile(compressed_file_path, "r") as zip_f:
            zip_f.extractall(directory)
    elif 'tar.gz' in basename or 'tgz' in basename:
        with tarfile.open(compressed_file_path) as f:
            f.extractall(directory)
def get_file_name_from_url(url: str) -> str:
    
    Return the file_name from a URL
    Parameters
    ----------
    url : str
        URL to extract file_name from
    Returns
    -------
    file_name : str
    
    parse = urlparse(url)
    return os.path.basename(parse.path)
urls = [
    'http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/training.tar.gz',
    'http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/validation.tar.gz',
    'http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/mmt16_task1_test.tar.gz'
]
directory = 'multi30k'
for url in urls:
    download_file(url, directory)
Explanation: Machine Translation with Huggingface Transformer
In this article we'll be leveraging Huggingface's Transformer on our machine translation task. The library provides thousands of pretrained models that we can use on our tasks. Apart from that, we'll also take a look at how to use its pre-built tokenizer and model architecture to train a model from scratch.
Data Preprocessing
We'll be using the Multi30k dataset to demonstrate using the transfomer model in a machine translation task. This German to English training dataset's size is around 29K, a moderate sized dataset so that we can get our results without waiting too long. We'll start off by downloading the raw dataset and extracting them. Feel free to swap this step with any other machine translation dataset.
End of explanation
!ls multi30k
!head multi30k/train.de
!head multi30k/train.en
Explanation: We print out the content in the data directory and some sample data.
End of explanation
def create_translation_data(
    source_input_path: str,
    target_input_path: str,
    output_path: str,
    delimiter: str = '\t',
    encoding: str = 'utf-8'
):
    
    Creates the paired source and target dataset from the separated ones.
    e.g. creates `train.tsv` from `train.de` and `train.en`
    
    with open(source_input_path, encoding=encoding) as f_source_in, \
         open(target_input_path, encoding=encoding) as f_target_in, \
         open(output_path, 'w', encoding=encoding) as f_out:
        for source_raw in f_source_in:
            source_raw = source_raw.strip()
            target_raw = f_target_in.readline().strip()
            if source_raw and target_raw:
                output_line = source_raw + delimiter + target_raw + '\n'
                f_out.write(output_line)
source_lang = 'de'
target_lang = 'en'
data_files = {}
for split in ['train', 'val', 'test']:
    source_input_path = os.path.join(directory, f'{split}.{source_lang}')
    target_input_path = os.path.join(directory, f'{split}.{target_lang}')
    output_path = f'{split}.tsv'
    create_translation_data(source_input_path, target_input_path, output_path)
    data_files[split] = [output_path]
data_files
dataset_dict = load_dataset(
    'csv',
    delimiter='\t',
    column_names=[source_lang, target_lang],
    data_files=data_files
)
dataset_dict
Explanation: The original dataset is splits the source and the target language into two separate files (e.g. train.de, train.en are the training dataset for German and English). This type of format is useful when we wish to train a tokenizer on top of the source or target language as we'll soon see.
On the other hand, having the source and target pair together in one single file makes it easier to load them in batches for training or evaluating our machine translation model. We'll create the paired dataset, and load the dataset. For loading the dataset, it will be helpful to have some basic understanding of Huggingface's dataset.
End of explanation
dataset_dict['train'][0]
Explanation: We can acsess the split, and each record/pair with the following syntax.
End of explanation
from transformers import AutoTokenizer
model_checkpoint = "Helsinki-NLP/opus-mt-de-en"
pretrained_tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
pretrained_tokenizer
Explanation: Pretrained Model
To get started we'll use the MarianMT pretrained model translation model.
First thing we'll do is to load the pre-trained tokenizer, using the from_pretrained syntax. This ensures we get the tokenizer and vocabulary corresponding to the model architecture for this specific checkpoint.
End of explanation
pretrained_tokenizer(dataset_dict['train']['de'][0])
# notice the last token id is 0, the end of sentence special token
pretrained_tokenizer.convert_ids_to_tokens(0)
pretrained_tokenizer(dataset_dict['train']['de'][0:2])
Explanation: We can pass a single record, or a list of records to huggingface's tokenizer. Then depending on the model, we might see different keys in the dictionary returned. For example, here, we have:
input_ids: The tokenizer converted our raw input text into numerical ids.
attention_mask Mask to avoid performing attention on padded token ids. As we haven't yet performed the padding step, the numbers are all showing up as 1, indicating they are not masked.
End of explanation
max_source_length = 128
max_target_length = 128
source_lang = "de"
target_lang = "en"
def batch_tokenize_fn(examples):
    
    Generate the input_ids and labels field for huggingface dataset/dataset dict.
    
    Truncation is enabled, so we cap the sentence to the max length, padding will be done later
    in a data collator, so pad examples to the longest length in the batch and not the whole dataset.
    
    sources = examples[source_lang]
    targets = examples[target_lang]
    model_inputs = pretrained_tokenizer(sources, max_length=max_source_length, truncation=True)
    # setup the tokenizer for targets,
    # huggingface expects the target tokenized ids to be stored in the labels field
    with pretrained_tokenizer.as_target_tokenizer():
        labels = pretrained_tokenizer(targets, max_length=max_target_length, truncation=True)
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs
tokenized_dataset_dict = dataset_dict.map(batch_tokenize_fn, batched=True, num_proc=8)
tokenized_dataset_dict
# printing out the tokenized data, to check for the newly added fields
tokenized_dataset_dict['train'][0]
Explanation: We can apply the tokenizers to our entire raw dataset, so this preprocessing will be a one time process. By passing the function to our dataset dict's map method, it will apply the same tokenizing step to all the splits in our data.
End of explanation
from transformers import AutoModelForSeq2SeqLM
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
pretrained_model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint).to(device)
print('# of parameters: ', pretrained_model.num_parameters())
pretrained_model
Explanation: Having prepared our dataset, we'll load the pre-trained model. Similar to the tokenizer, we can use the .from_pretrained method, and specify a valid huggingface model.
End of explanation
def generate_translation(model, tokenizer, example):
    print out the source, target and predicted raw text.
    source = example[source_lang]
    target = example[target_lang]
    input_ids = example['input_ids']
    input_ids = torch.LongTensor(input_ids).view(1, -1).to(model.device)
    generated_ids = model.generate(input_ids)
    prediction = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
    
    print('source: ', source)
    print('target: ', target)
    print('prediction: ', prediction)
example = tokenized_dataset_dict['train'][0]
generate_translation(pretrained_model, pretrained_tokenizer, example)
example = tokenized_dataset_dict['test'][0]
generate_translation(pretrained_model, pretrained_tokenizer, example)
Explanation: We can directly use this model to generate the translations, and eyeball the results.
End of explanation
from transformers import (
    AutoConfig,
    AutoModelForSeq2SeqLM,
    DataCollatorForSeq2Seq,
    EarlyStoppingCallback,
    Seq2SeqTrainingArguments,
    Seq2SeqTrainer
)
config_params = {
    'd_model': 256,
    'decoder_layers': 3,
    'decoder_attention_heads': 8,
    'decoder_ffn_dim': 512,
    'encoder_layers': 6,
    'encoder_attention_heads': 8,
    'encoder_ffn_dim': 512,
    'max_length': 128,
    'max_position_embeddings': 128
}
model_checkpoint = "Helsinki-NLP/opus-mt-de-en"
config = AutoConfig.from_pretrained(model_checkpoint, **config_params)
config
model = AutoModelForSeq2SeqLM.from_config(config)
print('# of parameters: ', model.num_parameters())
model
Explanation: Training Model From Scratch
The next section shows the steps for training the model parameters from scratch. Instead of directly instantiating the model using .from_pretrained method. We use the .from_config method, where we specify the configurations for a particular model architecture. The configuration will be created using .from_pretrained, as well as updating some of the configuration hyper parameters, where we opted for a smaller model for faster iteration.
End of explanation
batch_size = 128
args = Seq2SeqTrainingArguments(
    output_dir="test-translation",
    evaluation_strategy="epoch",
    learning_rate=0.0005,
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    weight_decay=0.01,
    save_total_limit=3,
    num_train_epochs=20,
    load_best_model_at_end=True,
    predict_with_generate=True,
    remove_unused_columns=True,
    fp16=True
)
data_collator = DataCollatorForSeq2Seq(pretrained_tokenizer)
callbacks = [EarlyStoppingCallback(early_stopping_patience=3)]
trainer = Seq2SeqTrainer(
    model,
    args,
    data_collator=data_collator,
    train_dataset=tokenized_dataset_dict["train"],
    eval_dataset=tokenized_dataset_dict["val"],
    callbacks=callbacks
)
Explanation: The huggingface library offers pre-built functionality to avoid writing the training logic from scratch. This step can be swapped out with other higher level trainer packages or even implementing our own logic. We setup the:
Seq2SeqTrainingArguments a class that contains all the attributes to customize the training. At the bare minimum, it requires one folder name, which will be used to save model checkpoint.
DataCollatorForSeq2Seq a helper class provided to batch our examples. Where the padding logic resides.
End of explanation
dataloader_train = trainer.get_train_dataloader()
batch = next(iter(dataloader_train))
batch
trainer_output = trainer.train()
trainer_output
Explanation: We can take a look at the batched examples. Understanding the output can be beneficial if we wish to customize the data collate function later.
attention_mask Padded tokens will be masked out with 0..
input_ids. Input ids are padded with the padding special tokens.
labels. By default -100 will be automatically ignored by PyTorch loss functions, hence it will the id to use when padding the labels.
End of explanation
def generate_translation(model, tokenizer, example):
    print out the source, target and predicted raw text.
    source = example[source_lang]
    target = example[target_lang]
    input_ids = tokenizer(source)['input_ids']
    input_ids = torch.LongTensor(input_ids).view(1, -1).to(model.device)
    generated_ids = model.generate(input_ids)
    prediction = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
    print('source: ', source)
    print('target: ', target)
    print('prediction: ', prediction)
example = dataset_dict['train'][0]
generate_translation(model, pretrained_tokenizer, example)
example = dataset_dict['test'][0]
generate_translation(model, pretrained_tokenizer, example)
Explanation: Similar to what we did before, we can use this model to generate the translations, and eyeball the results.
End of explanation
# use only the training set to train our tokenizer
split = 'train'
source_input_path = os.path.join(directory, f'{split}.{source_lang}')
target_input_path = os.path.join(directory, f'{split}.{target_lang}')
print(source_input_path, target_input_path)
bos_token = '<s>'
unk_token = '<unk>'
eos_token = '</s>'
pad_token = '<pad>'
special_tokens = [unk_token, bos_token, eos_token, pad_token]
tokenizer_params = {
    'min_frequency': 2,
    'vocab_size': 5000,
    'show_progress': False,
    'special_tokens': special_tokens
}
start_time = time.time()
source_tokenizer = ByteLevelBPETokenizer(lowercase=True)
source_tokenizer.train(source_input_path, **tokenizer_params)
target_tokenizer = ByteLevelBPETokenizer(lowercase=True)
target_tokenizer.train(target_input_path, **tokenizer_params)
end_time = time.time()
print('elapsed: ', end_time - start_time)
print('source vocab size: ', source_tokenizer.get_vocab_size())
print('target vocab size: ', target_tokenizer.get_vocab_size())
Explanation: Training Tokenizer and Model From Scratch
From our raw pair, we need to use or train a tokenizer to convert them into numerical indices. Here we'll be training our tokenizer from scratch using Huggingface's tokenizer. Feel free to swap this step out with other tokenization procedures, what's important is to leave rooms for special tokens such as the init token that represents the beginning of a sentence, the end of sentence token that represents the end of a sentence, unknown token, and padding token that pads sentence batches into equivalent length.
End of explanation
pad_token_id = source_tokenizer.token_to_id(pad_token)
eos_token_id = source_tokenizer.token_to_id(eos_token)
def batch_encode_fn(examples):
    sources = examples[source_lang]
    targets = examples[target_lang]
    input_ids = [encoding.ids + [eos_token_id] for encoding in source_tokenizer.encode_batch(sources)]
    labels = [encoding.ids + [eos_token_id] for encoding in target_tokenizer.encode_batch(targets)]
    examples['input_ids'] = input_ids
    examples['labels'] = labels
    return examples
dataset_dict_encoded = dataset_dict.map(batch_encode_fn, batched=True, num_proc=8)
dataset_dict_encoded
dataset_train = dataset_dict_encoded['train']
dataset_train[0]
Explanation: We'll perform this tokenization step for all our dataset up front, so we can do as little preprocessing as possible while feeding our dataset to model. Note that we do not perform the padding step at this stage.
End of explanation
class Seq2SeqDataCollator:
    
    def __init__(
        self,
        max_length: int,
        pad_token_id: int,
        pad_label_token_id: int = -100
    ):
        self.max_length = max_length
        self.pad_token_id = pad_token_id
        self.pad_label_token_id = pad_label_token_id
        
    def __call__(self, batch):
        source_batch = []
        source_len = []
        target_batch = []
        target_len = []
        for example in batch:
            source = example['input_ids']
            source_len.append(len(source))
            source_batch.append(source)
            target = example['labels']
            target_len.append(len(target))
            target_batch.append(target)
        source_padded = self.process_encoded_text(source_batch, source_len, self.pad_token_id)
        target_padded = self.process_encoded_text(target_batch, target_len, self.pad_label_token_id)
        attention_mask = generate_attention_mask(source_padded, self.pad_token_id)
        return {
            'input_ids': source_padded,
            'labels': target_padded,
            'attention_mask': attention_mask
        }
    def process_encoded_text(self, sequences, sequences_len, pad_token_id):
        sequences_max_len = np.max(sequences_len)
        max_length = min(sequences_max_len, self.max_length)
        padded_sequences = pad_sequences(sequences, max_length, pad_token_id)
        return torch.LongTensor(padded_sequences)
def generate_attention_mask(input_ids, pad_token_id):
    return (input_ids != pad_token_id).long()
    
def pad_sequences(sequences, max_length, pad_token_id):
    
    Pad the list of sequences (numerical token ids) to the same length.
    Sequence that are shorter than the specified ``max_len`` will be appended
    with the specified ``pad_token_id``. Those that are longer will be truncated.
    Parameters
    ----------
    sequences : list[int]
        List of numerical token ids.
    max_length : int
         Maximum length that all sequences will be truncated/padded to.
    pad_token_id : int
        Padding token index.
    Returns
    -------
    padded_sequences : 1d ndarray
    
    num_samples = len(sequences)
    padded_sequences = np.full((num_samples, max_length), pad_token_id)
    for i, sequence in enumerate(sequences):
        sequence = np.array(sequence)[:max_length]
        padded_sequences[i, :len(sequence)] = sequence
    return padded_sequences
Explanation: Given the custom tokenizer, we can also custom our data collate class that does the padding for input and labels.
End of explanation
config_params = {
    'd_model': 256,
    'decoder_layers': 3,
    'decoder_attention_heads': 8,
    'decoder_ffn_dim': 512,
    'encoder_layers': 6,
    'encoder_attention_heads': 8,
    'encoder_ffn_dim': 512,
    'max_length': 128,
    'max_position_embeddings': 128,
    'eos_token_id': eos_token_id,
    'pad_token_id': pad_token_id,
    'decoder_start_token_id': pad_token_id,
    "bad_words_ids": [
        [
            pad_token_id
        ]
    ],
    'vocab_size': source_tokenizer.get_vocab_size()
}
model_config = AutoConfig.from_pretrained(model_checkpoint, **config_params)
model_config
transformers_model = AutoModelForSeq2SeqLM.from_config(model_config)
print('# of parameters: ', transformers_model.num_parameters())
transformers_model
batch_size = 128
args = Seq2SeqTrainingArguments(
    output_dir="test-translation",
    evaluation_strategy="epoch",
    learning_rate=0.0005,
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    weight_decay=0.01,
    save_total_limit=3,
    num_train_epochs=20,
    load_best_model_at_end=True,
    predict_with_generate=True,
    remove_unused_columns=True,
    fp16=True
)
data_collator = Seq2SeqDataCollator(model_config.max_length, pad_token_id)
callbacks = [EarlyStoppingCallback(early_stopping_patience=3)]
trainer = Seq2SeqTrainer(
    transformers_model,
    args,
    train_dataset=dataset_dict_encoded["train"],
    eval_dataset=dataset_dict_encoded["val"],
    data_collator=data_collator,
    callbacks=callbacks
)
dataloader_train = trainer.get_train_dataloader()
next(iter(dataloader_train))
trainer_output = trainer.train()
trainer_output
def generate_translation(model, source_tokenizer, target_tokenizer, example):
    source = example[source_lang]
    target = example[target_lang]
    input_ids = source_tokenizer.encode(source).ids
    input_ids = torch.LongTensor(input_ids).view(1, -1).to(model.device)
    generated_ids = model.generate(input_ids)
    generated_ids = generated_ids[0].detach().cpu().numpy()
    prediction = target_tokenizer.decode(generated_ids)
    print('source: ', source)
    print('target: ', target)
    print('prediction: ', prediction)
example = dataset_dict['train'][0]
generate_translation(transformers_model, source_tokenizer, target_tokenizer, example)
example = dataset_dict['test'][0]
generate_translation(transformers_model, source_tokenizer, target_tokenizer, example)
Explanation: Given that we are using our own tokenizer instead of the pre-trained ones, we need to update a couple of other parameters in our config. The one that's worth pointing out is that this model starts generating with pad_token_id, that's why the decoder_start_token_id is the same as the pad_token_id.
Then rest of model training code should be the same as the ones in the previous section.
End of explanation
model_checkpoint = 'transformers_model'
transformers_model.save_pretrained(model_checkpoint)
transformers_model_loaded = transformers_model.from_pretrained(model_checkpoint).to(device)
example = dataset_dict['test'][0]
generate_translation(transformers_model_loaded, source_tokenizer, target_tokenizer, example)
Explanation: Confirming saving and loading the model gives us identical predictions.
End of explanation
len(dataset_dict_encoded['test'])
# we use a different data collator then the one we used for training and evaluating model
# replace -100 in the labels with other special tokens during inferencing
# as we can't decode them.
data_collator = Seq2SeqDataCollator(model_config.max_length, pad_token_id, pad_token_id)
data_loader = DataLoader(dataset_dict_encoded['test'], collate_fn=data_collator, batch_size=64)
data_loader
start = time.time()
rows = []
for example in data_loader:
    input_ids = example['input_ids']
    generated_ids = transformers_model.generate(input_ids.to(transformers_model.device))
    generated_ids = generated_ids.detach().cpu().numpy()
    predictions = target_tokenizer.decode_batch(generated_ids)
    labels = example['labels'].detach().cpu().numpy()
    targets = target_tokenizer.decode_batch(labels)
    sources = source_tokenizer.decode_batch(input_ids.detach().cpu().numpy())
    for source, target, prediction in zip(sources, targets, predictions):
        row = [source, target, prediction]
        rows.append(row)
end = time.time()
print('elapsed: ', end - start)
df_rows = pd.DataFrame(rows, columns=['source', 'target', 'prediction'])
df_rows
Explanation: As the last step, we'll write a inferencing function that performs batch scoring on a given dataset. Here we generate the predictions and save it in a pandas dataframe along with the source and the target.
End of explanation
<END_TASK>