
# 🇧🇬 BERT - Bulgarian Named Entity Recognition
The model [rmihaylov/bert-base-bg](https://huggingface.co/rmihaylov/bert-base-bg) fine-tuned on a Bulgarian subset of [wikiann](https://huggingface.co/datasets/wikiann).

## Usage
Import the libraries:
```python
from typing import List, Dict

import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
```

Firstly, you'll have to define these methods, since we are using a subword Tokenizer:
```python
def predict(
    text: str, 
    model: torch.nn.Module, 
    tokenizer: AutoTokenizer,
    labels_tags={
        0: "O",
        1: "B-PER", 2: "I-PER", 
        3: "B-ORG", 4: "I-ORG", 
        5: "B-LOC", 6: "I-LOC"
    }) -> List[Dict[str, str]]:
    tokens_data = tokenizer(text)
    tokens = tokenizer.convert_ids_to_tokens(tokens_data["input_ids"])
    words = subwords_to_words(tokens)

    input_ids = torch.LongTensor(tokens_data["input_ids"]).unsqueeze(0)
    attention_mask = torch.LongTensor(tokens_data["attention_mask"]).unsqueeze(0)

    out = model(input_ids, attention_mask=attention_mask).logits
    out = out.argmax(-1).squeeze(0).tolist()

    prediction = [labels_tags[idx] if idx in labels_tags else idx for idx in out]

    return merge_words_and_predictions(words, prediction)


def subwords_to_words(tokens: List[str]) -> List[str]:
    out_tokens = []
    curr_token = ""
    tags = []
    
    for token in tokens:
        if token == "[SEP]":
            curr_token = curr_token.replace("▁", "")
            out_tokens.append(curr_token)
            out_tokens.append("[SEP]")
            break

        if "▁" in token and curr_token == "":
            curr_token += token

        elif "▁" in token and curr_token != "":
            curr_token = curr_token.replace("▁", "")
            out_tokens.append(curr_token)
            curr_token = ""
            curr_token += token

        elif "▁" not in token:
            curr_token += token

    return out_tokens


def merge_words_and_predictions(words: List[str], entities: List[str]) -> List[Dict[str, str]]:
    result = []
    curr_word = []

    for i, (word, entity) in enumerate(zip(words[1:], entities[1:])):
        if "B-" in entity:
            if curr_word:
                curr_word = " ".join(curr_word)
                result.append({
                    "word": curr_word,
                    "entity_group": entities[i][2:]
                })
                curr_word = [word]
            else:
                curr_word.append(word)

        if "I-" in entity:
            curr_word.append(word)
        
        if "O" == entity:
            if curr_word:
                curr_word = " ".join(curr_word)
                result.append({
                    "word": curr_word,
                    "entity_group": entities[i][2:]
                })
            
            curr_word = []

    return result
```

Then, you should initialize the `AutoTokenizer` and `AutoModelForTokenClassification` objects:
```python
MODEL_ID = "auhide/bert-bg-ner"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForTokenClassification.from_pretrained(MODEL_ID)
```

Finally, you can call the `predict()` method from above like that:
```python
text = "Барух Спиноза е роден в Амстердам"
print(f"Input: {text}")
print("NERs:", predict(text, model=model, tokenizer=tokenizer))
```
```sh
Input: Барух Спиноза е роден в Амстердам
NERs: [{'word': 'Барух Спиноза', 'entity_group': 'PER'}, {'word': 'Амстердам', 'entity_group': 'LOC'}]
```

Note: There are three types of entities - `PER`, `ORG`, `LOC`.
{"language": ["bg"], "license": "cc-by-4.0", "datasets": ["wikiann"], "metrics": ["accuracy"], "inference": false}
transformers pytorch safetensors bert token-classification bg dataset:wikiann license:cc-by-4.0 autotrain_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #token-classification #bg #dataset-wikiann #license-cc-by-4.0 #autotrain_compatible #region-us 


# 🇧🇬 BERT - Bulgarian Named Entity Recognition
The model rmihaylov/bert-base-bg fine-tuned on a Bulgarian subset of wikiann.

## Usage
Import the libraries:


Firstly, you'll have to define these methods, since we are using a subword Tokenizer:


Then, you should initialize the 'AutoTokenizer' and 'AutoModelForTokenClassification' objects:


Finally, you can call the 'predict()' method from above like that:



Note: There are three types of entities - 'PER', 'ORG', 'LOC'.
# 🇧🇬 BERT - Bulgarian Named Entity Recognition
The model rmihaylov/bert-base-bg fine-tuned on a Bulgarian subset of wikiann. ## Usage
Import the libraries:


Firstly, you'll have to define these methods, since we are using a subword Tokenizer:


Then, you should initialize the 'AutoTokenizer' and 'AutoModelForTokenClassification' objects:


Finally, you can call the 'predict()' method from above like that:



Note: There are three types of entities - 'PER', 'ORG', 'LOC'.
TAGS
#transformers #pytorch #safetensors #bert #token-classification #bg #dataset-wikiann #license-cc-by-4.0 #autotrain_compatible #region-us 
 # 🇧🇬 BERT - Bulgarian Named Entity Recognition
The model rmihaylov/bert-base-bg fine-tuned on a Bulgarian subset of wikiann. ## Usage
Import the libraries:


Firstly, you'll have to define these methods, since we are using a subword Tokenizer:


Then, you should initialize the 'AutoTokenizer' and 'AutoModelForTokenClassification' objects:


Finally, you can call the 'predict()' method from above like that:



Note: There are three types of entities - 'PER', 'ORG', 'LOC'.
TAGS
#transformers #pytorch #safetensors #bert #token-classification #bg #dataset-wikiann #license-cc-by-4.0 #autotrain_compatible #region-us 
# 🇧🇬 BERT - Bulgarian Named Entity Recognition
The model rmihaylov/bert-base-bg fine-tuned on a Bulgarian subset of wikiann.## Usage
Import the libraries:


Firstly, you'll have to define these methods, since we are using a subword Tokenizer:


Then, you should initialize the 'AutoTokenizer' and 'AutoModelForTokenClassification' objects:


Finally, you can call the 'predict()' method from above like that:



Note: There are three types of entities - 'PER', 'ORG', 'LOC'.