savasy/bert-base-turkish-sentiment-cased

Bert-base Turkish Sentiment Model

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

Citation

Please cite if you use it in your study

@misc{yildirim2024finetuning,
      title={Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks}, 
      author={Savas Yildirim},
      year={2024},
      eprint={2401.17396},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}





@book{yildirim2021mastering,
  title={Mastering Transformers: Build state-of-the-art models from scratch with advanced natural language processing techniques},
  author={Yildirim, Savas and Asgari-Chenaghlu, Meysam},
  year={2021},
  publisher={Packt Publishing Ltd}
}

Dataset

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 (Beyazperde) 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. This dataset is also used by 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.

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)

[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

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

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

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)