From cffbb3d8edd8dcc16669e0f2cbc106e19b436f39 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sava=C5=9F=20Y=C4=B1ld=C4=B1r=C4=B1m?= Date: Mon, 11 May 2020 18:24:41 +0300 Subject: [PATCH] Update README.md (#4276) --- .../README.md | 105 +++++++++++++++++- 1 file changed, 100 insertions(+), 5 deletions(-) diff --git a/model_cards/savasy/bert-base-turkish-sentiment-cased/README.md b/model_cards/savasy/bert-base-turkish-sentiment-cased/README.md index a78f0d200b..d5387df2ea 100644 --- a/model_cards/savasy/bert-base-turkish-sentiment-cased/README.md +++ b/model_cards/savasy/bert-base-turkish-sentiment-cased/README.md @@ -1,4 +1,4 @@ -# 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. + +*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) +``` + + + + +