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
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Check out the documentation for more information.
Fine-tuned XLM-R Model for hebrew Sentiment Analysis
This is a fine-tuned XLM-R model for sentiment analysis in hebrew.
Model Details
- Model Name: XLM-R Sentiment Analysis
- Language: hebrew
- Fine-tuning Dataset: DGurgurov/hebrew_sa
Training Details
- Epochs: 20
- Batch Size: 32 (train), 64 (eval)
- Optimizer: AdamW
- Learning Rate: 5e-5
Performance Metrics
- Accuracy: 0.92106
- Macro F1: 0.90782
- Micro F1: 0.92106
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_hebrew_sentiment")
model = AutoModelForSequenceClassification.from_pretrained("DGurgurov/xlm-r_hebrew_sentiment")
License
[MIT]
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