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Update HooshvareLab/bert-base-parsbert-uncased (#4687)

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mBERT results added regarding NER datasets!
This commit is contained in:
Mehrdad Farahani
2020-06-01 14:27:00 +02:00
committed by GitHub
parent 74872c19d3
commit 036c2c6b02
1 changed files with 16 additions and 16 deletions
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model_cards/HooshvareLab/bert-base-parsbert-uncased/README.md
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@@ -28,8 +28,8 @@ The following table summarizes the F1 score obtained by ParsBERT as compared to
### Sentiment Analysis (SA) task ### Sentiment Analysis (SA) task
| Dataset | ParsBERT | Multilingual BERT | DeepSentiPers | | Dataset | ParsBERT | mBERT | DeepSentiPers |
|:--------------------------:|:---------:|:-----------------:|:-------------:| |:--------------------------:|:---------:|:-----:|:-------------:|
| Digikala User Comments | 81.74* | 80.74 | - | | Digikala User Comments | 81.74* | 80.74 | - |
| SnappFood User Comments | 88.12* | 87.87 | - | | SnappFood User Comments | 88.12* | 87.87 | - |
| SentiPers (Multi Class) | 71.11* | - | 69.33 | | SentiPers (Multi Class) | 71.11* | - | 69.33 |
@@ -39,18 +39,18 @@ The following table summarizes the F1 score obtained by ParsBERT as compared to
### Text Classification (TC) task ### Text Classification (TC) task
| Dataset | ParsBERT | Multilingual BERT | | Dataset | ParsBERT | mBERT |
|:-----------------:|:--------:|:-----------------:| |:-----------------:|:--------:|:-----:|
| Digikala Magazine | 93.59* | 90.72 | | Digikala Magazine | 93.59* | 90.72 |
| Persian News | 97.19* | 95.79 | | Persian News | 97.19* | 95.79 |
### Named Entity Recognition (NER) task ### Named Entity Recognition (NER) task
| Dataset | ParsBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF | | Dataset | ParsBERT | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
|:-------:|:--------:|:----------:|:--------------:|:----------:|:----------------:|:------------:| |:-------:|:--------:|:--------:|:----------:|:--------------:|:----------:|:----------------:|:------------:|
| PEYMA | 98.79* | - | 90.59 | - | 84.00 | - | | PEYMA | 93.10* | 86.64 | - | 90.59 | - | 84.00 | - |
| ARMAN | 93.10* | 89.9 | 84.03 | 86.55 | - | 77.45 | | ARMAN | 98.79* | 95.89 | 89.9 | 84.03 | 86.55 | - | 77.45 |
**If you tested ParsBERT on a public dataset and you want to add your results to the table above, open a pull request or contact us. Also make sure to have your code available online so we can add it as a reference** **If you tested ParsBERT on a public dataset and you want to add your results to the table above, open a pull request or contact us. Also make sure to have your code available online so we can add it as a reference**
@@ -66,10 +66,10 @@ config = AutoConfig.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased") tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
model = AutoModel.from_pretrained("HooshvareLab/bert-base-parsbert-uncased") model = AutoModel.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
text = "ما در هوشواره معتقدیم با انتقال صحیح دانش و آگاهی، همه‌ی افراد می‌توانند از ابزارهای هوشمند استفاده کنند. شعار ما هوش مصنوعی برای همه است." text = "ما در هوشواره معتقدیم با انتقال صحیح دانش و آگاهی، همه افراد می‌توانند از ابزارهای هوشمند استفاده کنند. شعار ما هوش مصنوعی برای همه است."
tokenizer.tokenize(text) tokenizer.tokenize(text)
>>> ['ما', 'در', 'هوش', '##واره', 'معتقدیم', 'با', 'انتقال', 'صحیح', 'دانش', 'و', 'اگاهی', '،', 'همهی', 'افراد', 'میتوانند', 'از', 'ابزارهای', 'هوشمند', 'استفاده', 'کنند', '.', 'شعار', 'ما', 'هوش', 'مصنوعی', 'برای', 'همه', 'است', '.'] >>> ['ما', 'در', 'هوش', '##واره', 'معتقدیم', 'با', 'انتقال', 'صحیح', 'دانش', 'و', 'اگاهی', '،', 'همه', 'افراد', 'میتوانند', 'از', 'ابزارهای', 'هوشمند', 'استفاده', 'کنند', '.', 'شعار', 'ما', 'هوش', 'مصنوعی', 'برای', 'همه', 'است', '.']
``` ```