Create README.md (#4202)

model_cards/savasy/bert-base-turkish-sentiment-cased/README.md

+# Details
+
+https://huggingface.co/savasy/bert-base-turkish-sentiment-cased
+
+This model is used for Sentiment Analysis, which is based on BERTurk for Turkish Language https://huggingface.co/dbmdz/bert-base-turkish-cased
+
+
+# Dataset
+
+We used product and movie dataset provided by the study [2] . This dataset includes
+movie and product reviews. The products are book, DVD, electronics, and kitchen.
+The movie dataset is taken from a cinema Web page (www.beyazperde.com) with
+5331 positive and 5331 negative sentences. Reviews in the Web page are marked in
+scale from 0 to 5 by the users who made the reviews. The study considered a review
+sentiment positive if the rating is equal to or bigger than 4, and negative if it is less
+or equal to 2. They also built Turkish product review dataset from an online retailer
+Web page. They constructed benchmark dataset consisting of reviews regarding some
+products (book, DVD, etc.). Likewise, reviews are marked in the range from 1 to 5,
+and majority class of reviews are 5. Each category has 700 positive and 700 negative
+reviews in which average rating of negative reviews is 2.27 and of positive reviews
+is 4.5.
+
+
+The dataset is used by following papers
+
+ 
+* 1 Yildirim, Savaş. (2020). Comparing Deep Neural Networks to Traditional Models for Sentiment Analysis in Turkish Language. 10.1007/978-981-15-1216-2_12. 
+* 2 Demirtas, Erkin and Mykola Pechenizkiy. 2013. Cross-lingual polarity detection with machine translation. In Proceedings of the Second International Workshop on Issues of Sentiment
+Discovery and Opinion Mining (WISDOM ’13)
+
+# Code Usage
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+model = AutoModelForSequenceClassification.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
+tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
+sa= pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)
+
+p= sa("bu telefon modelleri çok kaliteli , her parçası çok özel bence")
+print(p)
+#[{'label': 'LABEL_1', 'score': 0.9871089}]
+print (p[0]['label']=='LABEL_1')
+#True
+
+
+p= sa("Film çok kötü ve çok sahteydi")
+print(p)
+#[{'label': 'LABEL_0', 'score': 0.9975505}]
+print (p[0]['label']=='LABEL_1')
+#False
+```