Create ddd
Browse files
ddd
ADDED
|
@@ -0,0 +1,26 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# تثبيت المكتبات المطلوبة
|
| 2 |
+
# !pip install transformers torch
|
| 3 |
+
|
| 4 |
+
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
|
| 5 |
+
|
| 6 |
+
# نختار موديل فارسي جاهز
|
| 7 |
+
model_name = "HooshvareLab/bert-fa-base-uncased-sentiment-snappfood"
|
| 8 |
+
|
| 9 |
+
# تحميل التوكنيزر والموديل
|
| 10 |
+
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
| 11 |
+
model = AutoModelForSequenceClassification.from_pretrained(model_name)
|
| 12 |
+
|
| 13 |
+
# نعمل Pipeline للتحليل
|
| 14 |
+
sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
|
| 15 |
+
|
| 16 |
+
# نجرب نصوص بالفارسية
|
| 17 |
+
texts = [
|
| 18 |
+
"من این فیلم را خیلی دوست دارم", # جملة إيجابية
|
| 19 |
+
"این غذا افتضاح بود", # جملة سلبية
|
| 20 |
+
"امروز یک روز معمولی است" # جملة محايدة
|
| 21 |
+
]
|
| 22 |
+
|
| 23 |
+
for t in texts:
|
| 24 |
+
result = sentiment_pipeline(t)
|
| 25 |
+
print(f"متن: {t}")
|
| 26 |
+
print(f"نتیجه: {result}\n")
|