README.md

---
license: cc
---

This is a classifier fine_tuned from camemBERT that takes as input a text and a question and returns 1 if the text is helpful to answer the question and 0 else. 
the input should be formatted as tokenized_paragraph + sep_token + tokenized_question.

class QuestionAnswerDataset(Dataset):
    def __init__(self, dataframe, tokenizer, max_length=512):
        self.dataframe = dataframe
        self.tokenizer = tokenizer
        self.max_length = max_length

    def __len__(self):
        return len(self.dataframe)

    def __getitem__(self, idx):
        # Extract data
        row = self.dataframe.iloc[idx]
        paragraph = row['paragraph']
        is_positive = np.random.random()
        if is_positive > 0.5:
            is_positive = 1
        else:
            is_positive = 0

        if is_positive:
            question = row['positive_questions'][np.random.randint(len(row['positive_questions']))]
            label = 1
        else:
            question = row['negative_questions'][np.random.randint(len(row['negative_questions']))]
            label = 0

        # print('paragraph', paragraph)
        # print('question', question)

        # Tokenize
        tokenized_paragraph = self.tokenizer(
            paragraph,
            truncation=True,
            max_length=self.max_length,
            return_tensors="pt"
        )
        tokenized_question = self.tokenizer(
            question,
            truncation=True,
            max_length=self.max_length,
            return_tensors="pt"
        )
        total_length = tokenized_paragraph['input_ids'].shape[1] + tokenized_question['input_ids'].shape[1]+2
        if total_length > self.max_length:
            tokenized_paragraph['input_ids'] = tokenized_paragraph['input_ids'][:, :self.max_length - tokenized_question['input_ids'].shape[1]-2]
            tokenized_paragraph['attention_mask'] = tokenized_paragraph['attention_mask'][:, :self.max_length - tokenized_question['input_ids'].shape[1]-2]


        # print('-'*100)
        # print('tokenized_paragraph', tokenized_paragraph)
        # print('tokenized_question', tokenized_question)
        # print('total_length', total_length)
        # print('-'*100)

        sep_token = torch.tensor([[self.tokenizer.sep_token_id]]).to(tokenized_paragraph['input_ids'].device)


        # Concatenate tokenized inputs
        tokenized_input_ids = torch.cat((tokenized_paragraph['input_ids'], sep_token, tokenized_question['input_ids']),
                                        dim=1)
        tokenized_attention_mask = torch.cat(
            [tokenized_paragraph['attention_mask'], torch.ones_like(sep_token), tokenized_question['attention_mask']],
            dim=1)

        # print("tokenized_input_ids shape:", tokenized_input_ids.shape)
        # print("tokenized_attention_mask shape:", tokenized_attention_mask.shape)

        # Make sure the length does not exceed max_length
        if tokenized_input_ids.size(1) > self.max_length:
            tokenized_input_ids = tokenized_input_ids[:, :self.max_length]
            tokenized_attention_mask = tokenized_attention_mask[:, :self.max_length]

        return tokenized_input_ids.squeeze(0), tokenized_attention_mask.squeeze(0), torch.tensor(label)


# Tokenizer
tokenizer = AutoTokenizer.from_pretrained("camembert/camembert-large")

#split test_train test_size = 0.2
from sklearn.model_selection import train_test_split

train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)


# Dataset
train_dataset = QuestionAnswerDataset(train_df, tokenizer)
test_dataset = QuestionAnswerDataset(test_df, tokenizer)

def custom_collate_fn(batch):
    input_ids = [item[0] for item in batch]
    attention_masks = [item[1] for item in batch]
    labels = torch.tensor([item[2] for item in batch])

    input_ids_padded = pad_sequence(input_ids, batch_first=True, padding_value=tokenizer.pad_token_id)
    attention_masks_padded = pad_sequence(attention_masks, batch_first=True, padding_value=0)

    return input_ids_padded, attention_masks_padded, labels


# DataLoader
train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True, collate_fn=custom_collate_fn)
test_dataloader = DataLoader(test_dataset, batch_size=64, shuffle=True, collate_fn=custom_collate_fn)

# Assuming 'myDataloader' is your DataLoader
for i, (input_ids, attention_masks, labels) in enumerate(train_dataloader):
    print(f"Batch {i+1}")
    print("Input IDs:", input_ids)
    print("Input IDs Shape:", input_ids.shape)
    print("Attention Masks:", attention_masks)
    print("Attention Masks Shape:", attention_masks.shape)
    print("Labels:", labels)
    print("Labels Shape:", labels.shape)
    print("-" * 50)

    # Optionally, stop after the first few batches
    if i == 1:  # Change this number to control how many batches to print
        break


# Model (for binary classification)
camembertModel = AutoModelForSequenceClassification.from_pretrained("camembert/camembert-large", num_labels=1)