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language: fa |
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# BertNER |
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This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from [ARMAN](https://github.com/HaniehP/PersianNER), [PEYMA](http://nsurl.org/2019-2/tasks/task-7-named-entity-recognition-ner-for-farsi/), and [WikiANN](https://elisa-ie.github.io/wikiann/) that covered ten types of entities: |
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- Date (DAT) |
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- Event (EVE) |
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- Facility (FAC) |
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- Location (LOC) |
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- Money (MON) |
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- Organization (ORG) |
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- Percent (PCT) |
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- Person (PER) |
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- Product (PRO) |
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- Time (TIM) |
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## Dataset Information |
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| | Records | B-DAT | B-EVE | B-FAC | B-LOC | B-MON | B-ORG | B-PCT | B-PER | B-PRO | B-TIM | I-DAT | I-EVE | I-FAC | I-LOC | I-MON | I-ORG | I-PCT | I-PER | I-PRO | I-TIM | |
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|:------|----------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:| |
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| Train | 29133 | 1423 | 1487 | 1400 | 13919 | 417 | 15926 | 355 | 12347 | 1855 | 150 | 1947 | 5018 | 2421 | 4118 | 1059 | 19579 | 573 | 7699 | 1914 | 332 | |
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| Valid | 5142 | 267 | 253 | 250 | 2362 | 100 | 2651 | 64 | 2173 | 317 | 19 | 373 | 799 | 387 | 717 | 270 | 3260 | 101 | 1382 | 303 | 35 | |
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| Test | 6049 | 407 | 256 | 248 | 2886 | 98 | 3216 | 94 | 2646 | 318 | 43 | 568 | 888 | 408 | 858 | 263 | 3967 | 141 | 1707 | 296 | 78 | |
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## Evaluation |
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The following tables summarize the scores obtained by model overall and per each class. |
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**Overall** |
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| Model | accuracy | precision | recall | f1 | |
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|:----------:|:--------:|:---------:|:--------:|:--------:| |
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| Bert | 0.995086 | 0.953454 | 0.961113 | 0.957268 | |
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**Per entities** |
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| | number | precision | recall | f1 | |
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|:---: |:------: |:---------: |:--------: |:--------: | |
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| DAT | 407 | 0.860636 | 0.864865 | 0.862745 | |
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| EVE | 256 | 0.969582 | 0.996094 | 0.982659 | |
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| FAC | 248 | 0.976190 | 0.991935 | 0.984000 | |
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| LOC | 2884 | 0.970232 | 0.971914 | 0.971072 | |
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| MON | 98 | 0.905263 | 0.877551 | 0.891192 | |
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| ORG | 3216 | 0.939125 | 0.954602 | 0.946800 | |
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| PCT | 94 | 1.000000 | 0.968085 | 0.983784 | |
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| PER | 2645 | 0.965244 | 0.965974 | 0.965608 | |
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| PRO | 318 | 0.981481 | 1.000000 | 0.990654 | |
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| TIM | 43 | 0.692308 | 0.837209 | 0.757895 | |
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## How To Use |
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You use this model with Transformers pipeline for NER. |
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### Installing requirements |
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```bash |
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pip install transformers |
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``` |
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### How to predict using pipeline |
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```python |
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from transformers import AutoTokenizer |
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from transformers import AutoModelForTokenClassification # for pytorch |
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from transformers import TFAutoModelForTokenClassification # for tensorflow |
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from transformers import pipeline |
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model_name_or_path = "HooshvareLab/bert-fa-zwnj-base-ner" |
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) |
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model = AutoModelForTokenClassification.from_pretrained(model_name_or_path) # Pytorch |
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# model = TFAutoModelForTokenClassification.from_pretrained(model_name_or_path) # Tensorflow |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "در سال ۲۰۱۳ درگذشت و آندرتیکر و کین برای او مراسم یادبود گرفتند." |
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ner_results = nlp(example) |
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print(ner_results) |
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``` |
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## Questions? |
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Post a Github issue on the [ParsNER Issues](https://github.com/hooshvare/parsner/issues) repo. |
