README.md

metadata

library_name: peft
license: apache-2.0
base_model: xlm-roberta-base
tags:
  - base_model:adapter:xlm-roberta-base
  - lora
  - transformers
metrics:
  - accuracy
  - f1
model-index:
  - name: bengali-code-mix-sentiment-lora
    results: []
datasets:
  - Swarnadeep-28/bn_code_mix_sentiment_dataset
pipeline_tag: text-classification

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

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.6325	1.0	1001	0.8349	0.6982	0.6974
0.7065	2.0	2002	0.7734	0.7096	0.7093
0.6849	3.0	3003	0.7649	0.7171	0.7149
0.6452	4.0	4004	0.7603	0.7176	0.7180
0.669	5.0	5005	0.7597	0.7206	0.7206

Framework versions

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