Refactored the README.md file (#4427)
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@ -5,12 +5,12 @@ https://huggingface.co/savasy/bert-base-turkish-sentiment-cased
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This model is used for Sentiment Analysis, which is based on BERTurk for Turkish Language https://huggingface.co/dbmdz/bert-base-turkish-cased
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# Dataset
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## Dataset
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The dataset is taken from the studies [2] and [3] and merged.
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The dataset is taken from the studies [[2]](#paper-2) and [[3]](#paper-3), and merged.
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* The study [2] gathered movie and product reviews. The products are book, DVD, electronics, and kitchen.
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The movie dataset is taken from a cinema Web page (www.beyazperde.com) with
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The movie dataset is taken from a cinema Web page ([Beyazperde](www.beyazperde.com)) with
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5331 positive and 5331 negative sentences. Reviews in the Web page are marked in
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scale from 0 to 5 by the users who made the reviews. The study considered a review
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sentiment positive if the rating is equal to or bigger than 4, and negative if it is less
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@ -19,9 +19,9 @@ Web page. They constructed benchmark dataset consisting of reviews regarding som
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products (book, DVD, etc.). Likewise, reviews are marked in the range from 1 to 5,
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and majority class of reviews are 5. Each category has 700 positive and 700 negative
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reviews in which average rating of negative reviews is 2.27 and of positive reviews
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is 4.5. This dataset is also used the study [1]
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is 4.5. This dataset is also used by the study [[1]](#paper-1).
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* 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.
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* 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.
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*Merged Dataset*
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@ -32,20 +32,21 @@ is 4.5. This dataset is also used the study [1]
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| 32000 |train.tsv|
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| *48290* |*total*|
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### The dataset is used by following papers
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The dataset is used by following papers
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* 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.
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* 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
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<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.
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<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
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Discovery and Opinion Mining (WISDOM ’13)
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* [3] 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
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# Training
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<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
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```
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## Training
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```shell
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export GLUE_DIR="./sst-2-newall"
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export TASK_NAME=SST-2
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python3 run_glue.py \
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--model_type bert \
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@ -59,88 +60,79 @@ python3 run_glue.py \
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--learning_rate 2e-5 \
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--num_train_epochs 3.0 \
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--output_dir "./model"
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```
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## Results
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> 05/10/2020 17:00:43 - INFO - transformers.trainer - \*\*\*\*\* Running Evaluation \*\*\*\*\*
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> 05/10/2020 17:00:43 - INFO - transformers.trainer - Num examples = 7999
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> 05/10/2020 17:00:43 - INFO - transformers.trainer - Batch size = 8
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> Evaluation: 100% 1000/1000 [00:34<00:00, 29.04it/s]
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> 05/10/2020 17:01:17 - INFO - \_\_main__ - \*\*\*\*\* Eval results sst-2 \*\*\*\*\*
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> 05/10/2020 17:01:17 - INFO - \_\_main__ - acc = 0.9539942492811602
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> 05/10/2020 17:01:17 - INFO - \_\_main__ - loss = 0.16348013816401363
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Accuracy is about **95.4%**
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# Results
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## Code Usage
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> 05/10/2020 17:00:43 - INFO - transformers.trainer - ***** Running Evaluation *****
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> 05/10/2020 17:00:43 - INFO - transformers.trainer - Num examples = 7999
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> 05/10/2020 17:00:43 - INFO - transformers.trainer - Batch size = 8
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>Evaluation: 100% 1000/1000 [00:34<00:00, 29.04it/s]
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>05/10/2020 17:01:17 - INFO - __main__ - ***** Eval results sst-2 *****
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>05/10/2020 17:01:17 - INFO - __main__ - acc = 0.9539942492811602
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>05/10/2020 17:01:17 - INFO - __main__ - loss = 0.16348013816401363
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Accuracy is about *%95.4*
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# Code Usage
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```
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```python
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
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model = AutoModelForSequenceClassification.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
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tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
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sa= pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)
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p= sa("bu telefon modelleri çok kaliteli , her parçası çok özel bence")
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p = sa("bu telefon modelleri çok kaliteli , her parçası çok özel bence")
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print(p)
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#[{'label': 'LABEL_1', 'score': 0.9871089}]
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print (p[0]['label']=='LABEL_1')
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#True
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# [{'label': 'LABEL_1', 'score': 0.9871089}]
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print(p[0]['label'] == 'LABEL_1')
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# True
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p= sa("Film çok kötü ve çok sahteydi")
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p = sa("Film çok kötü ve çok sahteydi")
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print(p)
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#[{'label': 'LABEL_0', 'score': 0.9975505}]
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print (p[0]['label']=='LABEL_1')
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#False
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# [{'label': 'LABEL_0', 'score': 0.9975505}]
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print(p[0]['label'] == 'LABEL_1')
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# False
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```
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# Test your data
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## Test
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### Data
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Suppose your file has lots of lines of comment and label (1 or 0) at the end (tab seperated)
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> comment1 ... \t label
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> comment2 ... \t label
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> comment1 ... \t label
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> comment2 ... \t label
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> ...
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### Code
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```
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```python
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
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f="/path/to/your/file/yourfile.tsv"
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model = AutoModelForSequenceClassification.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
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tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
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sa= pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)
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sa = pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)
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i,crr=0,0
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for line in open(f):
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lines=line.strip().split("\t")
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if len(lines)==2:
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i=i+1
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if i%100==0:
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print(i)
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pred= sa(lines[0])
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pred=pred[0]["label"].split("_")[1]
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if pred== lines[1]:
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crr=crr+1
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input_file = "/path/to/your/file/yourfile.tsv"
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i, crr = 0, 0
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for line in open(input_file):
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lines = line.strip().split("\t")
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if len(lines) == 2:
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i = i + 1
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if i%100 == 0:
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print(i)
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pred = sa(lines[0])
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pred = pred[0]["label"].split("_")[1]
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if pred == lines[1]:
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crr = crr + 1
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print(crr, i, crr/i)
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```
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