Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use adamw_torch 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
No log	1.0	291	0.1533	0.9467	0.9466
0.2224	2.0	582	0.2004	0.9467	0.9469
0.2224	3.0	873	0.2178	0.9553	0.9553
0.0393	4.0	1164	0.2400	0.9553	0.9552
0.0393	5.0	1455	0.2481	0.9519	0.9520

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Tokenizers 0.21.0

How to use:

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_path = "ehab215/egyptian_sentiment_analysis"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)

# Ensure model is on GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Step 2: Prepare test examples
examples = [
    add any examples you would
]

# Tokenize the examples
inputs = tokenizer(examples, truncation=True, padding=True, return_tensors="pt", max_length=256)
inputs = {key: val.to(device) for key, val in inputs.items()}

# Step 3: Make predictions
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predictions = torch.argmax(logits, dim=-1).cpu().numpy()

# Step 4: Interpret results
label_map = {0: "negative", 1: "neutral", 2: "positive"}
predicted_labels = [label_map[p] for p in predictions]

# Display results
for text, label in zip(examples, predicted_labels):
    print(f"Text: {text}")
    print(f"Predicted Sentiment: {label}")
    print("-" * 50)