This repository contains a BERT-based model enhanced with a BiLSTM layer for sequence classification tasks. The model allows you to leverage the power of a pre-trained BERT model, combined with the benefits of a BiLSTM, to handle sequence-level tasks like sentiment analysis, text classification, and more.

Features:

Pre-trained BERT model: Leverage BERT's embeddings for robust language understanding.
BiLSTM layer: Capture sequential dependencies in both directions (forward and backward).
Customizable freezing of BERT layers: Choose which layers of the BERT model you want to freeze, and whether to freeze from the start or the end.
Inference without labels: Get logits directly for inference in production, with no need for labels.
Logging for better debugging: Includes logging for important events like model initialization, layer freezing, and inference.

Installation:

Install the necessary dependencies:

pip install transformers torch
Clone this repository and navigate to the project folder:

bash
1git clone <repository-url> 2cd <project-folder>

Configuration:

The model's behavior can be customized using the following configuration options:

freeze_bert: If True, the BERT model's layers will be frozen according to the specified settings.
freeze_n_layers: An integer that defines the number of layers to freeze.
freeze_from_start: If True, freeze the first n layers from the start; if False, freeze the last n layers from the end.
concat_layers: Number of BERT layers to concatenate for the final sequence output.
pooling: Type of pooling to apply. Options: 'last', 'mean', etc.

Example usage for configuring the model:

python

from transformers import BertTokenizer
from modeling_bert_bilstm import BertBiLSTMForSequenceClassification, BertBiLSTMConfig

# Configure the model
config = BertBiLSTMConfig(
    bert_model_name="bert-base-uncased",
    freeze_bert=True,
    freeze_n_layers=10,
    freeze_from_start=False  # Freeze the last 10 layers
)

# Initialize the model
model = BertBiLSTMForSequenceClassification(config)

# Print model's freeze summary
freeze_summary = model.get_freeze_summary()
print(freeze_summary)

Training the Model:

To train the model, you need to prepare your dataset and use standard PyTorch training loops. Here’s an outline of how you might train the model:

python

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

# Create DataLoader, model, optimizer, etc.
train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)
optimizer = AdamW(model.parameters(), lr=1e-5)

for epoch in range(num_epochs):
    model.train()
    for batch in train_dataloader:
        input_ids = batch["input_ids"]
        attention_mask = batch["attention_mask"]
        labels = batch["labels"]

        optimizer.zero_grad()
        output = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
        loss = output["loss"]
        loss.backward()
        optimizer.step()

Inference (Prediction without Labels):

For serving the model in production, the model can be used for inference without needing labels.

Example Forward Pass for Inference:

python

import torch

# Example input (input_ids, attention_mask)
input_ids = torch.tensor([[101, 2054, 2003, 102]])  # Example tokenized input
attention_mask = torch.tensor([[1, 1, 1, 1]])       # Example attention mask

# Get logits for prediction (no labels required)
logits = model(input_ids=input_ids, attention_mask=attention_mask)
print(logits)

Logging:

This model includes logging to help with debugging and monitoring during training and inference. Logs include information such as:

Initialization of the BERT model.
Freezing layers.
Inference start and completion.

To configure logging:

python

import logging

# Set up logging
logging.basicConfig(level=logging.INFO,
                    format='%(asctime)s - %(levelname)s - %(message)s',
                    handlers=[logging.StreamHandler()])

logger = logging.getLogger(__name__)

# Example log messages
logger.info("Model initialized with BERT model: %s", config.bert_model_name)
logger.info(f"Freezing the top {config.freeze_n_layers} layers of BERT.")

Model Freezing Configuration:

You can customize which layers of BERT to freeze. The freeze_n_layers parameter allows you to freeze a specific number of layers either from the start or the end of the BERT model:

freeze_from_start=True: Freeze the first n layers.
freeze_from_start=False: Freeze the last n layers.

Example of Freezing Layers:

python

config = BertBiLSTMConfig(
    freeze_bert=True,
    freeze_n_layers=10,  # Freeze the last 10 layers
    freeze_from_start=False  # Freeze from the end
)

Model Summary:

You can view a summary of which layers are frozen and which are trainable by using the get_freeze_summary() method:

python

freeze_summary = model.get_freeze_summary()
print(freeze_summary)

Example output:

python

[
  {"layer": "bert.encoder.layer.0", "trainable": False},
  {"layer": "bert.encoder.layer.1", "trainable": False},
  {"layer": "bert.encoder.layer.2", "trainable": True},
  {"layer": "bert.encoder.layer.3", "trainable": True},
  ...
]

Notes:

This model is production-ready for serving via APIs like FastAPI or Flask for real-time predictions.
Make sure to handle logging and exception management properly in production.

License:

This repository is licensed under the MIT License. See the LICENSE file for more information.

Amal17/NusaBERT-concate-BiGRU-NusaTranslate-senti

BERT + BiLSTM Model for Sequence Classification

Overview

Features:

Installation:

Configuration:

Training the Model:

Inference (Prediction without Labels):

Example Forward Pass for Inference:

Logging:

Model Freezing Configuration:

Example of Freezing Layers:

Model Summary:

Notes:

License:

Amal17/
NusaBERT-concate-BiGRU-NusaTranslate-senti