metadata

base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment
metrics:
  - accuracy
model-index:
  - name: result
    results: []
language:
  - ar
  - en
library_name: transformers
pipeline_tag: text-classification

SentimentArEng

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

Loss: 0.502831
Accuracy: 0.798512

inference with pipeline

from transformers import pipeline
model_path = "Noor0/SentimentArEng"
sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
sentiment_task("تعامل الموظفين كان أقل من المتوقع")

output:
[{'label': 'negative', 'score': 0.9905518293380737}]

Training and evaluation data

Training set: 114,885 records
evaluation data: 12,765 records

Training procedure

Training Loss	Epoch	Validation Loss	Accuracy
0.4511	2.0	0.502831	0.7985
0.3655	3.0	0.576118	0.7954
0.3019	4.0	0.625391	0.7985
0.2466	5.0	0.835689	0.7979

Training hyperparameters

The following hyperparameters were used during training:
- learning_rate=2e-5
- num_train_epochs=20
- weight_decay=0.01
- batch_size=16,

Framework versions

Transformers 4.35.0
Pytorch 2.0.0
Datasets 2.11.0
Tokenizers 0.14.1