Sentiment Analysis Models
Collection
Collection of XLM-R-based Sentiment Analysis models trained on the data with two labels - positive and negative.
•
25 items
•
Updated
YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/model-cards#model-card-metadata)
Fine-tuned XLM-R Model for sundanese Sentiment Analysis
This is a fine-tuned XLM-R model for sentiment analysis in sundanese.
Model Details
- Model Name: XLM-R Sentiment Analysis
- Language: sundanese
- Fine-tuning Dataset: DGurgurov/sundanese_sa
Training Details
- Epochs: 20
- Batch Size: 32 (train), 64 (eval)
- Optimizer: AdamW
- Learning Rate: 5e-5
Performance Metrics
- Accuracy: 0.88158
- Macro F1: 0.88106
- Micro F1: 0.88158
Usage
To use this model, you can load it with the Hugging Face Transformers library:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("DGurgurov/xlm-r_sundanese_sentiment")
model = AutoModelForSequenceClassification.from_pretrained("DGurgurov/xlm-r_sundanese_sentiment")
License
[MIT]
- Downloads last month
- 9
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.