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README.md ADDED
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+ ---
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+ language: fa
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+ ---
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+
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+
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+ # RobertaNER
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+
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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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+
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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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+
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+
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+ ## Dataset Information
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+
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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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+
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+
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+ ## Evaluation
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+
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+ The following tables summarize the scores obtained by model overall and per each class.
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+
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+ **Overall**
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+
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+ | Model | accuracy | precision | recall | f1 |
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+ |:----------:|:--------:|:---------:|:--------:|:--------:|
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+ | Roberta | 0.994849 | 0.949816 | 0.960235 | 0.954997 |
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+
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+
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+ **Per entities**
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+
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+ | | number | precision | recall | f1 |
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+ |:---: |:------: |:---------: |:--------: |:--------: |
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+ | DAT | 407 | 0.844869 | 0.869779 | 0.857143 |
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+ | EVE | 256 | 0.948148 | 1.000000 | 0.973384 |
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+ | FAC | 248 | 0.957529 | 1.000000 | 0.978304 |
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+ | LOC | 2884 | 0.965422 | 0.968100 | 0.966759 |
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+ | MON | 98 | 0.937500 | 0.918367 | 0.927835 |
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+ | ORG | 3216 | 0.943662 | 0.958333 | 0.950941 |
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+ | PCT | 94 | 1.000000 | 0.968085 | 0.983784 |
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+ | PER | 2646 | 0.957030 | 0.959562 | 0.958294 |
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+ | PRO | 318 | 0.963636 | 1.000000 | 0.981481 |
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+ | TIM | 43 | 0.739130 | 0.790698 | 0.764045 |
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+
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+
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+ ## How To Use
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+ You use this model with Transformers pipeline for NER.
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+
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+ ### Installing requirements
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+
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+ ```bash
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+ pip install transformers
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+ ```
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+
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+ ### How to predict using pipeline
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+
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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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+
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+
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+ model_name_or_path = "HooshvareLab/roberta-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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+
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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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+
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+ ner_results = nlp(example)
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+ print(ner_results)
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+ ```
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+
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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.
config.json ADDED
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+ {
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+ "architectures": [
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+ "RobertaForTokenClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "eos_token_id": 2,
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+ "finetuning_task": "ner",
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "O",
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+ "1": "B-DAT",
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+ "2": "B-EVE",
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+ "3": "B-FAC",
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+ "4": "B-LOC",
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+ "5": "B-MON",
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+ "6": "B-ORG",
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+ "7": "B-PCT",
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+ "8": "B-PER",
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+ "9": "B-PRO",
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+ "10": "B-TIM",
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+ "11": "I-DAT",
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+ "12": "I-EVE",
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+ "13": "I-FAC",
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+ "14": "I-LOC",
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+ "15": "I-MON",
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+ "16": "I-ORG",
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+ "17": "I-PCT",
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+ "18": "I-PER",
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+ "19": "I-PRO",
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+ "20": "I-TIM"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "label2id": {
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+ "B-DAT": 1,
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+ "B-EVE": 2,
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+ "B-FAC": 3,
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+ "B-LOC": 4,
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+ "B-MON": 5,
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+ "B-ORG": 6,
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+ "B-PCT": 7,
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+ "B-PER": 8,
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+ "B-PRO": 9,
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+ "B-TIM": 10,
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+ "I-DAT": 11,
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+ "I-EVE": 12,
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+ "I-FAC": 13,
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+ "I-LOC": 14,
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+ "I-MON": 15,
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+ "I-ORG": 16,
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+ "I-PCT": 17,
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+ "I-PER": 18,
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+ "I-PRO": 19,
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+ "I-TIM": 20,
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+ "O": 0
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 514,
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+ "model_type": "roberta",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 1,
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+ "position_embedding_type": "absolute",
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+ "transformers_version": "4.5.0.dev0",
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+ "type_vocab_size": 1,
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+ "use_cache": true,
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+ "vocab_size": 42000
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+ }
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