bengali-code-mix-sentiment-lora

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bengali-code-mix-sentiment-lora

This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7597
• Accuracy: 0.7206
• F1: 0.7206

Model description

This model is a LoRA Parameter-Efficient Fine-Tuned version of xlm-roberta-base for sentiment analysis on Bengali–English code-mixed text (commonly found in social media posts, comments, and tweets).

• Task: Text Classification (Sentiment Analysis)
• Languages: Bengali (Romanized) + English
• Classes: positive, negative, neutral
• Fine-tuning method: LoRA (PEFT)
• Dataset: Bengali-English Code-Mixed Sentiment Dataset

This model enables efficient, low-resource fine-tuning while maintaining competitive performance for code-mixed sentiment classification.

How to Use

Inference Example

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel
import torch

# Load tokenizer & model
model_id = "Swarnadeep-28/bengali-code-mix-sentiment-lora"
tokenizer = AutoTokenizer.from_pretrained(model_id)
base_model = AutoModelForSequenceClassification.from_pretrained("xlm-roberta-base", num_labels=3)
model = PeftModel.from_pretrained(base_model, model_id)

# Example text
text = "Aaj match ta khub bhalo chilo! Loved it."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

with torch.no_grad():
    logits = model(**inputs).logits
pred = torch.argmax(logits, dim=-1).item()
labels = ["negative", "neutral", "positive"]
print("Predicted label:", labels[pred])

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0005
• train_batch_size: 16
• eval_batch_size: 32
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• PEFT 0.17.1
• Transformers 4.56.1
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.0