from datasets import load_dataset

raw_datasets = load_dataset("squad")

raw_datasets

DatasetDict({
    train: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers'],
        num_rows: 87599
    })
    validation: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers'],
        num_rows: 10570
    })
})

print("Context: ", raw_datasets["train"][0]["context"])
print("Question: ", raw_datasets["train"][0]["question"])
print("Answer: ", raw_datasets["train"][0]["answers"])

Context: 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.'
Question: 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?'
Answer: {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]}

raw_datasets["train"].filter(lambda x: len(x["answers"]["text"]) != 1)

Dataset({
    features: ['id', 'title', 'context', 'question', 'answers'],
    num_rows: 0
})

print(raw_datasets["validation"][0]["answers"])
print(raw_datasets["validation"][2]["answers"])

{'text': ['Denver Broncos', 'Denver Broncos', 'Denver Broncos'], 'answer_start': [177, 177, 177]}
{'text': ['Santa Clara, California', "Levi's Stadium", "Levi's Stadium in the San Francisco Bay Area at Santa Clara, California."], 'answer_start': [403, 355, 355]}

print(raw_datasets["validation"][2]["context"])
print(raw_datasets["validation"][2]["question"])

'Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24–10 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi\'s Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the "golden anniversary" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as "Super Bowl L"), so that the logo could prominently feature the Arabic numerals 50.'
'Where did Super Bowl 50 take place?'

from transformers import AutoTokenizer

model_checkpoint = "bert-base-cased"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

tokenizer.is_fast

True

[CLS] question [SEP] context [SEP]

context = raw_datasets["train"][0]["context"]
question = raw_datasets["train"][0]["question"]

inputs = tokenizer(question, context)
tokenizer.decode(inputs["input_ids"])

'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Architecturally, '
'the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin '
'Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms '
'upraised with the legend " Venite Ad Me Omnes ". Next to the Main Building is the Basilica of the Sacred '
'Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a '
'replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette '
'Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 statues '
'and the Gold Dome ), is a simple, modern stone statue of Mary. [SEP]'

inputs = tokenizer(
    question,
    context,
    max_length=100,
    truncation="only_second",
    stride=50,
    return_overflowing_tokens=True,
)

for ids in inputs["input_ids"]:
    print(tokenizer.decode(ids))

'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend " Venite Ad Me Omnes ". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basi [SEP]'
'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend " Venite Ad Me Omnes ". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin [SEP]'
'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 [SEP]'
'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP]. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 statues and the Gold Dome ), is a simple, modern stone statue of Mary. [SEP]'

inputs = tokenizer(
    question,
    context,
    max_length=100,
    truncation="only_second",
    stride=50,
    return_overflowing_tokens=True,
    return_offsets_mapping=True,
)
inputs.keys()

dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'offset_mapping', 'overflow_to_sample_mapping'])

inputs["overflow_to_sample_mapping"]

[0, 0, 0, 0]

inputs = tokenizer(
    raw_datasets["train"][2:6]["question"],
    raw_datasets["train"][2:6]["context"],
    max_length=100,
    truncation="only_second",
    stride=50,
    return_overflowing_tokens=True,
    return_offsets_mapping=True,
)

print(f"The 4 examples gave {len(inputs['input_ids'])} features.")
print(f"Here is where each comes from: {inputs['overflow_to_sample_mapping']}.")

'The 4 examples gave 19 features.'
'Here is where each comes from: [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3].'

answers = raw_datasets["train"][2:6]["answers"]
start_positions = []
end_positions = []

for i, offset in enumerate(inputs["offset_mapping"]):
    sample_idx = inputs["overflow_to_sample_mapping"][i]
    answer = answers[sample_idx]
    start_char = answer["answer_start"][0]
    end_char = answer["answer_start"][0] + len(answer["text"][0])
    sequence_ids = inputs.sequence_ids(i)

    # Find the start and end of the context
    idx = 0
    while sequence_ids[idx] != 1:
        idx += 1
    context_start = idx
    while sequence_ids[idx] == 1:
        idx += 1
    context_end = idx - 1

    # If the answer is not fully inside the context, label is (0, 0)
    if offset[context_start][0] > start_char or offset[context_end][1] < end_char:
        start_positions.append(0)
        end_positions.append(0)
    else:
        # Otherwise it's the start and end token positions
        idx = context_start
        while idx <= context_end and offset[idx][0] <= start_char:
            idx += 1
        start_positions.append(idx - 1)

        idx = context_end
        while idx >= context_start and offset[idx][1] >= end_char:
            idx -= 1
        end_positions.append(idx + 1)

start_positions, end_positions

([83, 51, 19, 0, 0, 64, 27, 0, 34, 0, 0, 0, 67, 34, 0, 0, 0, 0, 0],
 [85, 53, 21, 0, 0, 70, 33, 0, 40, 0, 0, 0, 68, 35, 0, 0, 0, 0, 0])

idx = 0
sample_idx = inputs["overflow_to_sample_mapping"][idx]
answer = answers[sample_idx]["text"][0]

start = start_positions[idx]
end = end_positions[idx]
labeled_answer = tokenizer.decode(inputs["input_ids"][idx][start : end + 1])

print(f"Theoretical answer: {answer}, labels give: {labeled_answer}")

'Theoretical answer: the Main Building, labels give: the Main Building'

idx = 4
sample_idx = inputs["overflow_to_sample_mapping"][idx]
answer = answers[sample_idx]["text"][0]

decoded_example = tokenizer.decode(inputs["input_ids"][idx])
print(f"Theoretical answer: {answer}, decoded example: {decoded_example}")

'Theoretical answer: a Marian place of prayer and reflection, decoded example: [CLS] What is the Grotto at Notre Dame? [SEP] Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend " Venite Ad Me Omnes ". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grot [SEP]'

max_length = 384
stride = 128


def preprocess_training_examples(examples):
    questions = [q.strip() for q in examples["question"]]
    inputs = tokenizer(
        questions,
        examples["context"],
        max_length=max_length,
        truncation="only_second",
        stride=stride,
        return_overflowing_tokens=True,
        return_offsets_mapping=True,
        padding="max_length",
    )

    offset_mapping = inputs.pop("offset_mapping")
    sample_map = inputs.pop("overflow_to_sample_mapping")
    answers = examples["answers"]
    start_positions = []
    end_positions = []

    for i, offset in enumerate(offset_mapping):
        sample_idx = sample_map[i]
        answer = answers[sample_idx]
        start_char = answer["answer_start"][0]
        end_char = answer["answer_start"][0] + len(answer["text"][0])
        sequence_ids = inputs.sequence_ids(i)

        # Find the start and end of the context
        idx = 0
        while sequence_ids[idx] != 1:
            idx += 1
        context_start = idx
        while sequence_ids[idx] == 1:
            idx += 1
        context_end = idx - 1

        # If the answer is not fully inside the context, label is (0, 0)
        if offset[context_start][0] > start_char or offset[context_end][1] < end_char:
            start_positions.append(0)
            end_positions.append(0)
        else:
            # Otherwise it's the start and end token positions
            idx = context_start
            while idx <= context_end and offset[idx][0] <= start_char:
                idx += 1
            start_positions.append(idx - 1)

            idx = context_end
            while idx >= context_start and offset[idx][1] >= end_char:
                idx -= 1
            end_positions.append(idx + 1)

    inputs["start_positions"] = start_positions
    inputs["end_positions"] = end_positions
    return inputs

train_dataset = raw_datasets["train"].map(
    preprocess_training_examples,
    batched=True,
    remove_columns=raw_datasets["train"].column_names,
)
len(raw_datasets["train"]), len(train_dataset)

(87599, 88729)

def preprocess_validation_examples(examples):
    questions = [q.strip() for q in examples["question"]]
    inputs = tokenizer(
        questions,
        examples["context"],
        max_length=max_length,
        truncation="only_second",
        stride=stride,
        return_overflowing_tokens=True,
        return_offsets_mapping=True,
        padding="max_length",
    )

    sample_map = inputs.pop("overflow_to_sample_mapping")
    example_ids = []

    for i in range(len(inputs["input_ids"])):
        sample_idx = sample_map[i]
        example_ids.append(examples["id"][sample_idx])

        sequence_ids = inputs.sequence_ids(i)
        offset = inputs["offset_mapping"][i]
        inputs["offset_mapping"][i] = [
            o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)
        ]

    inputs["example_id"] = example_ids
    return inputs

validation_dataset = raw_datasets["validation"].map(
    preprocess_validation_examples,
    batched=True,
    remove_columns=raw_datasets["validation"].column_names,
)
len(raw_datasets["validation"]), len(validation_dataset)

(10570, 10822)

small_eval_set = raw_datasets["validation"].select(range(100))
trained_checkpoint = "distilbert-base-cased-distilled-squad"

tokenizer = AutoTokenizer.from_pretrained(trained_checkpoint)
eval_set = small_eval_set.map(
    preprocess_validation_examples,
    batched=True,
    remove_columns=raw_datasets["validation"].column_names,
)

tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

import torch
from transformers import AutoModelForQuestionAnswering

eval_set_for_model = eval_set.remove_columns(["example_id", "offset_mapping"])
eval_set_for_model.set_format("torch")

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
batch = {k: eval_set_for_model[k].to(device) for k in eval_set_for_model.column_names}
trained_model = AutoModelForQuestionAnswering.from_pretrained(trained_checkpoint).to(
    device
)

with torch.no_grad():
    outputs = trained_model(**batch)

start_logits = outputs.start_logits.cpu().numpy()
end_logits = outputs.end_logits.cpu().numpy()

import collections

example_to_features = collections.defaultdict(list)
for idx, feature in enumerate(eval_set):
    example_to_features[feature["example_id"]].append(idx)

import numpy as np

n_best = 20
max_answer_length = 30
predicted_answers = []

for example in small_eval_set:
    example_id = example["id"]
    context = example["context"]
    answers = []

    for feature_index in example_to_features[example_id]:
        start_logit = start_logits[feature_index]
        end_logit = end_logits[feature_index]
        offsets = eval_set["offset_mapping"][feature_index]

        start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()
        end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()
        for start_index in start_indexes:
            for end_index in end_indexes:
                # Skip answers that are not fully in the context
                if offsets[start_index] is None or offsets[end_index] is None:
                    continue
                # Skip answers with a length that is either < 0 or > max_answer_length.
                if (
                    end_index < start_index
                    or end_index - start_index + 1 > max_answer_length
                ):
                    continue

                answers.append(
                    {
                        "text": context[offsets[start_index][0] : offsets[end_index][1]],
                        "logit_score": start_logit[start_index] + end_logit[end_index],
                    }
                )

    best_answer = max(answers, key=lambda x: x["logit_score"])
    predicted_answers.append({"id": example_id, "prediction_text": best_answer["text"]})

from datasets import load_metric

metric = load_metric("squad")

theoretical_answers = [
    {"id": ex["id"], "answers": ex["answers"]} for ex in small_eval_set
]

print(predicted_answers[0])
print(theoretical_answers[0])

{'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}
{'id': '56be4db0acb8001400a502ec', 'answers': {'text': ['Denver Broncos', 'Denver Broncos', 'Denver Broncos'], 'answer_start': [177, 177, 177]}}

metric.compute(predictions=predicted_answers, references=theoretical_answers)

{'exact_match': 83.0, 'f1': 88.25}

from tqdm.auto import tqdm


def compute_metrics(start_logits, end_logits, features, examples):
    example_to_features = collections.defaultdict(list)
    for idx, feature in enumerate(features):
        example_to_features[feature["example_id"]].append(idx)

    predicted_answers = []
    for example in tqdm(examples):
        example_id = example["id"]
        context = example["context"]
        answers = []

        # Loop through all features associated with that example
        for feature_index in example_to_features[example_id]:
            start_logit = start_logits[feature_index]
            end_logit = end_logits[feature_index]
            offsets = features[feature_index]["offset_mapping"]

            start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()
            end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()
            for start_index in start_indexes:
                for end_index in end_indexes:
                    # Skip answers that are not fully in the context
                    if offsets[start_index] is None or offsets[end_index] is None:
                        continue
                    # Skip answers with a length that is either < 0 or > max_answer_length
                    if (
                        end_index < start_index
                        or end_index - start_index + 1 > max_answer_length
                    ):
                        continue

                    answer = {
                        "text": context[offsets[start_index][0] : offsets[end_index][1]],
                        "logit_score": start_logit[start_index] + end_logit[end_index],
                    }
                    answers.append(answer)

        # Select the answer with the best score
        if len(answers) > 0:
            best_answer = max(answers, key=lambda x: x["logit_score"])
            predicted_answers.append(
                {"id": example_id, "prediction_text": best_answer["text"]}
            )
        else:
            predicted_answers.append({"id": example_id, "prediction_text": ""})

    theoretical_answers = [{"id": ex["id"], "answers": ex["answers"]} for ex in examples]
    return metric.compute(predictions=predicted_answers, references=theoretical_answers)

compute_metrics(start_logits, end_logits, eval_set, small_eval_set)

{'exact_match': 83.0, 'f1': 88.25}

model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)

from huggingface_hub import notebook_login

notebook_login()

huggingface-cli login

from transformers import TrainingArguments

args = TrainingArguments(
    "bert-finetuned-squad",
    evaluation_strategy="no",
    save_strategy="epoch",
    learning_rate=2e-5,
    num_train_epochs=3,
    weight_decay=0.01,
    fp16=True,
    push_to_hub=True,
)

from transformers import Trainer

trainer = Trainer(
    model=model,
    args=args,
    train_dataset=train_dataset,
    eval_dataset=validation_dataset,
    tokenizer=tokenizer,
)
trainer.train()

predictions, _ = trainer.predict(validation_dataset)
start_logits, end_logits = predictions
compute_metrics(start_logits, end_logits, validation_dataset, raw_datasets["validation"])

{'exact_match': 81.18259224219489, 'f1': 88.67381321905516}

trainer.push_to_hub(commit_message="Training complete")

'https://huggingface.co/sgugger/bert-finetuned-squad/commit/9dcee1fbc25946a6ed4bb32efb1bd71d5fa90b68'

from torch.utils.data import DataLoader
from transformers import default_data_collator

train_dataset.set_format("torch")
validation_set = validation_dataset.remove_columns(["example_id", "offset_mapping"])
validation_set.set_format("torch")

train_dataloader = DataLoader(
    train_dataset,
    shuffle=True,
    collate_fn=default_data_collator,
    batch_size=8,
)
eval_dataloader = DataLoader(
    validation_set, collate_fn=default_data_collator, batch_size=8
)

model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)

from torch.optim import AdamW

optimizer = AdamW(model.parameters(), lr=2e-5)

from accelerate import Accelerator

accelerator = Accelerator(fp16=True)
model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

from transformers import get_scheduler

num_train_epochs = 3
num_update_steps_per_epoch = len(train_dataloader)
num_training_steps = num_train_epochs * num_update_steps_per_epoch

lr_scheduler = get_scheduler(
    "linear",
    optimizer=optimizer,
    num_warmup_steps=0,
    num_training_steps=num_training_steps,
)

from huggingface_hub import Repository, get_full_repo_name

model_name = "bert-finetuned-squad-accelerate"
repo_name = get_full_repo_name(model_name)
repo_name

'sgugger/bert-finetuned-squad-accelerate'

output_dir = "bert-finetuned-squad-accelerate"
repo = Repository(output_dir, clone_from=repo_name)

from tqdm.auto import tqdm
import torch

progress_bar = tqdm(range(num_training_steps))

for epoch in range(num_train_epochs):
    # Training
    model.train()
    for step, batch in enumerate(train_dataloader):
        outputs = model(**batch)
        loss = outputs.loss
        accelerator.backward(loss)

        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
        progress_bar.update(1)

    # Evaluation
    model.eval()
    start_logits = []
    end_logits = []
    accelerator.print("Evaluation!")
    for batch in tqdm(eval_dataloader):
        with torch.no_grad():
            outputs = model(**batch)

        start_logits.append(accelerator.gather(outputs.start_logits).cpu().numpy())
        end_logits.append(accelerator.gather(outputs.end_logits).cpu().numpy())

    start_logits = np.concatenate(start_logits)
    end_logits = np.concatenate(end_logits)
    start_logits = start_logits[: len(validation_dataset)]
    end_logits = end_logits[: len(validation_dataset)]

    metrics = compute_metrics(
        start_logits, end_logits, validation_dataset, raw_datasets["validation"]
    )
    print(f"epoch {epoch}:", metrics)

    # Save and upload
    accelerator.wait_for_everyone()
    unwrapped_model = accelerator.unwrap_model(model)
    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)
    if accelerator.is_main_process:
        tokenizer.save_pretrained(output_dir)
        repo.push_to_hub(
            commit_message=f"Training in progress epoch {epoch}", blocking=False
        )

accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)

from transformers import pipeline

# Replace this with your own checkpoint
model_checkpoint = "huggingface-course/bert-finetuned-squad"
question_answerer = pipeline("question-answering", model=model_checkpoint)

context = """
🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration
between them. It's straightforward to train your models with one before loading them for inference with the other.
"""
question = "Which deep learning libraries back 🤗 Transformers?"
question_answerer(question=question, context=context)

{'score': 0.9979003071784973,
 'start': 78,
 'end': 105,
 'answer': 'Jax, PyTorch and TensorFlow'}