Update README.md (#4276)

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

-# Details
+# Bert-base Turkish Sentiment Model
 
 https://huggingface.co/savasy/bert-base-turkish-sentiment-cased
 
@@ -7,8 +7,9 @@ This model is used for Sentiment Analysis, which is based on BERTurk for Turkish
 
 # 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 dataset is taken from the studies [2] and [3] and merged.
+
+* The study [2] gathered 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
@@ -18,16 +19,70 @@ Web page. They constructed benchmark dataset consisting of reviews regarding som
 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.
+is 4.5. This dataset is also used the study [1]
 
+* The study[3] collected tweet dataset. They proposed a new approach for automatically classifying the sentiment of microblog messages. The proposed approach is based on utilizing robust feature representation and fusion. 
 
-The dataset is used by following papers
+*Merged Dataset* 
+
+| *size*   | *data* |
+|--------|----|
+|   8000 |dev.tsv|
+|   8262 |test.tsv|
+|  32000 |train.tsv|
+|  *48290* |*total*|
 
+
+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)
+* Hayran, A.,   Sert, M. (2017), "Sentiment Analysis on Microblog Data based on Word Embedding and Fusion Techniques", IEEE 25th Signal Processing and Communications Applications Conference (SIU 2017), Belek, Turkey
+
+# Training
+
+```
+export GLUE_DIR="./sst-2-newall"
+export TASK_NAME=SST-2
+ 
+
+python3 run_glue.py \
+  --model_type bert \
+  --model_name_or_path dbmdz/bert-base-turkish-uncased\
+  --task_name "SST-2" \
+  --do_train \
+  --do_eval \
+  --data_dir "./sst-2-newall" \
+  --max_seq_length 128 \
+  --per_gpu_train_batch_size 32 \
+  --learning_rate 2e-5 \
+  --num_train_epochs 3.0 \
+  --output_dir "./model"
+
+```
+
+
+
+
+# Results
+
+> 05/10/2020 17:00:43 - INFO - transformers.trainer -   ***** Running Evaluation *****
+
+> 05/10/2020 17:00:43 - INFO - transformers.trainer -     Num examples = 7999
+
+> 05/10/2020 17:00:43 - INFO - transformers.trainer -     Batch size = 8
+
+>Evaluation: 100% 1000/1000 [00:34<00:00, 29.04it/s]
+
+>05/10/2020 17:01:17 - INFO - __main__ -   ***** Eval results sst-2 *****
+
+>05/10/2020 17:01:17 - INFO - __main__ -     acc = 0.9539942492811602
 
+>05/10/2020 17:01:17 - INFO - __main__ -     loss = 0.16348013816401363
+
+
+Accuracy is about *%95.4*
 # Code Usage
 
 ```
@@ -49,3 +104,43 @@ print(p)
 print (p[0]['label']=='LABEL_1')
 #False
 ```
+
+# Test your data
+
+Suppose your file has lots of lines of comment and label (1 or 0) at the end  (tab seperated)
+
+> comment1 ... \t label
+
+> comment2 ... \t label
+ 
+> ...
+
+
+
+```
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+
+f="/path/to/your/file/yourfile.tsv"
+model = AutoModelForSequenceClassification.from_pretrained(folder)
+tokenizer = AutoTokenizer.from_pretrained(folder)
+sa= pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)
+
+i,crr=0,0
+for line in open(f):
+ lines=line.strip().split("\t")
+ if len(lines)==2:
+  i=i+1
+  if i%100==0:
+   print(i)
+  pred= sa(lines[0])
+  pred=pred[0]["label"].split("_")[1]
+  if pred== lines[1]:
+   crr=crr+1
+
+print(crr, i, crr/i)
+```
+
+
+
+
+