Project Journey #2: 🛠️ Coding, Failing, and Learning with AI Law Shield ⚖️

Welcome back to my AI journey, where I stumbled, learned, and maybe cried a little! 😂

1. Diving into the Code: The Good, The Bad, and The Ugly

This time, I got my hands dirty by coding the first version of my AI model. Spoiler alert: I achieved an accuracy of just 0.18945%! 🎯 (Ouch! I guess even my toaster could do better 🤖🍞).

Let's dive into the code and see what went wrong.

# Initializing BERT for sequence classification
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=5)

What’s happening here?
I'm using BERT, the superstar transformer model, to classify the danger level of legal contracts on a scale of 1 to 5. 📄

def preprocess_data(dataframe, tokenizer):
    texts = dataframe['texte'].tolist()
    labels = [label - 1 for label in dataframe['niveau_de_danger'].tolist()]
    encoded_data = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
    return encoded_data, labels

Why preprocess data?
I’ve tokenized the contract texts for BERT to digest (like breaking down a complex contract into easier-to-understand clauses). 🍽️

2. Training My Model: And… It Crashed and Burned 💥

def train_model(model, train_loader, num_epochs=5):
    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
    model.to(device)
    optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5)

    for epoch in range(num_epochs):
        model.train()
        for batch in train_loader:
            optimizer.zero_grad()
            input_ids, attention_mask, labels = [b.to(device) for b in batch]
            outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
            accuracy = (outputs.logits.argmax(dim=-1) == labels).float().mean()
            loss = outputs.loss
            loss.backward()
            optimizer.step()
        print(f'Epoch {epoch + 1}/{num_epochs}, Loss: {loss.item()}')
    print(f'Final Loss: {loss:.4f}, Accuracy: {accuracy:.4f}')

This function trains BERT to classify contracts, but let’s just say it didn’t pass the bar exam 😬. The low accuracy told me that my model was basically guessing randomly.

3. Evaluating the Model: Reality Check 🧑‍⚖️

def evaluate_model(model, test_loader):
    model.eval()
    predictions = []
    true_labels = []
    with torch.no_grad():
        for batch in test_loader:
            input_ids, attention_mask, labels = [b.to(device) for b in batch]
            outputs = model(input_ids, attention_mask=attention_mask)
            _, predicted = torch.max(outputs.logits, dim=-1)
            predictions.extend(predicted.cpu().tolist())
            true_labels.extend(labels.cpu().tolist())
    return classification_report(true_labels, predictions)

After running this, I got a brutal classification report that screamed, "You need more data, buddy!" 📉

4. The Root Cause: My Dataset Needs a Lawyer-Grade Makeover 📊

After some reflection, I realized the real issue was my dataset. It’s like trying to learn law from a pamphlet instead of an encyclopedia. 📚

I need to get my hands on a large, reliable, and indexed dataset that can better train the model. If anyone knows where to find high-quality legal datasets, I’m all ears! 👂

5. Annotating Contracts (A Work in Progress) ✍️

def annotate_contract(model, tokenizer, contract_text):
    inputs = tokenizer(contract_text, padding=True, truncation=True, return_tensors='pt')
    with torch.no_grad():
        outputs = model(**inputs)
        _, predicted = torch.max(outputs.logits, dim=-1)
    danger_level = predicted.item() + 1
    problematic_sections = analyze_problematic_sections(contract_text, danger_level)

    return {
        'danger_level': danger_level,
        'problematic_sections': problematic_sections
    }

This function is supposed to analyze the legal contract and predict the danger level, but as you might guess, it’s not ready to replace your lawyer just yet. 🧐

Next Steps: A Better Dataset and Model Tuning 📈

I’m planning to go on a treasure hunt for a better dataset. Once I have more data, I’ll revisit model training, tweak hyperparameters, and hopefully get a model that can actually understand legal jargon! ⚖️

Until next time, may your accuracy be ever in your favor! 🚀

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