Notes

To implement a document classification model using BERT with data in an Excel file containing two columns (context and target), you can follow these steps using Python, pandas, and the Hugging Face Transformers library. First, make sure you have the necessary libraries installed:

```bash
pip install transformers torch pandas scikit-learn openpyxl
```

Here's a step-by-step guide:

1. **Load and Preprocess Data**:
Load the data from your Excel file and preprocess it. You can use the `pandas` library to read the Excel file:

```python
import pandas as pd

# Load the Excel file
df = pd.read_excel("your_data.xlsx")

# Split the data into features (context) and labels (target)
texts = df['context'].tolist()
labels = df['target'].tolist()
```

2. **Load Pre-trained BERT Model**:
Load a pre-trained BERT model and tokenizer. You can use the same code as mentioned earlier:

```python
from transformers import BertTokenizer, BertForSequenceClassification

model_name = "bert-base-uncased"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name, num_labels=num_classes)
```

Make sure to replace `num_classes` with the number of unique labels in your dataset.

3. **Tokenize and Prepare Data**:
Tokenize your text data and prepare it for training:

```python
encoded_data = tokenizer(
texts,
truncation=True,
padding=True,
return_tensors="pt",
max_length=128 # Adjust as needed
)

input_ids = encoded_data["input_ids"]
attention_mask = encoded_data["attention_mask"]
```

4. **Split Data into Train and Test Sets**:
Split your data into training and testing sets for model evaluation:

```python
from sklearn.model_selection import train_test_split

train_texts, test_texts, train_labels, test_labels = train_test_split(texts, labels, test_size=0.2, random_state=42)
```

5. **Fine-tune the Model**:
Define your training loop and fine-tune the BERT model on your dataset. You can use libraries like PyTorch or TensorFlow to handle this. Here's a simplified example using PyTorch:

```python
import torch
from torch.utils.data import DataLoader, TensorDataset

train_dataset = TensorDataset(input_ids, attention_mask, torch.tensor(train_labels))
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
loss_fn = torch.nn.CrossEntropyLoss()

for epoch in range(epochs):
model.train()
for batch in train_dataloader:
input_ids, attention_mask, labels = batch
optimizer.zero_grad()
outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
loss = outputs.loss
loss.backward()
optimizer.step()
```

6. **Evaluate the Model**:
After training, evaluate the model on the test set to assess its performance:

```python
model.eval()
with torch.no_grad():
# Perform evaluation and calculate metrics (e.g., accuracy, F1-score)
```

7. **Make Predictions**:
Use the trained model to make predictions on new documents, just like in the previous example.

8. **Save and Deploy**:
Finally, save the trained model and deploy it for inference as needed.

This code assumes that you have a dataset in an Excel file with two columns (context and target). You can adapt and extend this code according to your specific dataset and requirements.