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Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/savasy/bert-base-turkish-sentiment-cased/README.md

README.md

@@ -1,15 +1,19 @@
+---
+language: tr
+---
+# Bert-base Turkish Sentiment Model
 
-# Details
-This model is used for Sentiment Analysis based on BERTurk for Turkish Language https://huggingface.co/dbmdz/bert-base-turkish-cased
+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
 
+## Dataset
 
+The dataset is taken from the studies [[2]](#paper-2) and [[3]](#paper-3), and merged.
 
-We used product and movie dataset provided by a 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
+* 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 ([Beyazperde](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
@@ -18,13 +22,120 @@ 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 by the study [[1]](#paper-1).
 
+* The study [[3]](#paper-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*|
 
- 
-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
+### The dataset is used by following papers
+
+<a id="paper-1">[1]</a> Yildirim, Savaş. (2020). Comparing Deep Neural Networks to Traditional Models for Sentiment Analysis in Turkish Language. 10.1007/978-981-15-1216-2_12. 
+
+<a id="paper-2">[2]</a> 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)
+
+<a id="paper-3">[3]</a> 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
+
+```shell
+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
+
+```python
+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
+```
+
+
+## Test
+### 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  
+> ...
+
+### Code
+
+```python
+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)
+
+input_file = "/path/to/your/file/yourfile.tsv"
+
+i, crr = 0, 0
+for line in open(input_file):
+    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)
+```