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xlm-roberta-multilingual-sentiment
kanika103/xlm-roberta-multilingual-sentiment
xlm-roberta-multilingual-sentiment is a text classification model from kanika103. Use it when you need a label for a piece of text. The card lists the license as apache-2.0.
This is a LoRA fine-tuned version of FacebookAI/xlm-roberta-base for multilingual sentiment classification (Negative, Neutral, Positive).
Access
Public
Updated Mar 7, 2026
Repo size
35.5 MB
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0
Public
Repo makeup
Click a slice to open those files.
.json17.1 MB · 48%
At a glance
- Task
- Text Classification
- License
- apache-2.0
- Access
- Public
- Created
- Mar 7, 2026
- Updated
- Mar 7, 2026
- SHA
- 0d448893
Try a prompt
Model card
From the Hugging Face model README
Multilingual Tweet Sentiment Model (LoRA Fine-Tuned)
This is a LoRA fine-tuned version of FacebookAI/xlm-roberta-base for multilingual sentiment classification (Negative, Neutral, Positive).
Trained on: cardiffnlp/tweet_sentiment_multilingual ("all" split)
- Task: Sequence classification (3 labels)
- Training: 3 epochs, LoRA (r=16, alpha=32), fp16 on T4 GPU
- Test Accuracy: ~0.6519 (early checkpoint; full training ke baad better expected)
- Languages supported: English, Spanish, Hindi, French, Chinese, Portuguese, etc.
How to use
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel
base_model = "FacebookAI/xlm-roberta-base"
repo = "kanika103/xlm-roberta-multilingual-sentiment"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSequenceClassification.from_pretrained(base_model, num_labels=3)
model = PeftModel.from_pretrained(model, repo)
- Task
- Text Classification
- License
- apache-2.0
- Languages
- en, es, hi, fr, zh, pt
- Created
- Mar 7, 2026
- Updated
- Mar 7, 2026