Create README.md

README.md

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+---
+language:
+- bn
+metrics:
+- f1
+pipeline_tag: token-classification
+---
+# Bangla-Person-Name-Extractor
+This repository contains the implementation of a Bangla Person Name Extractor model which is able to extract Person name entities from a given sentence. We approached it as a token classification task i.e. tagging each token with either a Person's name or not. We leveraged the [BanglaBERT](http://https://github.com/csebuetnlp/banglabert) model for our task, finetuning it for a binary classification task using a custom-prepare dataset. We have deployed the model into huggingface for easier access and use case.
+
+
+# Datasets
+We used two datasets to train and evaluate our pipeline. 
+1. [Bengali-NER/annotated data at master · Rifat1493/Bengali-NER](http://https://github.com/Rifat1493/Bengali-NER/tree/master/annotated%20data)
+2. [banglakit/bengali-ner-data](http://https://raw.githubusercontent.com/banglakit/bengali-ner-data/master/main.jsonl)
+
+The annotation formats for both datasets were quite different, so we had to preprocess both of them before merging them. Please refer to [this notebook](https://github.com/MBMMurad/Bangla-Person-Name-Extractor/blob/main/prepare-dataset.ipynb) for preparing the dataset as required.
+
+# Training and Evaluation
+We treated this problem as a token classification task.So it seemed perfect to finetune BanglaBERT model for our purpose. [BanglaBERT ](https://huggingface.co/csebuetnlp/banglabert)is an [ELECTRA](https://openreview.net/pdf?id=r1xMH1BtvB) discriminator model pretrained with the Replaced Token Detection (RTD) objective. Finetuned models using this checkpoint achieve state-of-the-art results on many of the NLP tasks in bengali.
+We mainly finetuned two checkpoints of BanglaBERT.
+1. [BanglaBERT](https://huggingface.co/csebuetnlp/banglabert)
+2. [BanglaEERT small](https://huggingface.co/csebuetnlp/banglabert_small) 
+
+BanglaBERT performed better than BanglaBERT small ( 83% F1 score vs 79% F1 score on the test set) .
+Please refer to [this notebook](https://github.com/MBMMurad/Bangla-Person-Name-Extractor/blob/main/Training%20Notebook%20%3A%20Person%20Name%20Extractor%20using%20BanglaBERT.ipynb) to see the training process.
+
+**Quantitative results**
+Please refer to [this notebook](https://github.com/MBMMurad/Bangla-Person-Name-Extractor/blob/main/Inference%20and%20Evaluation%20Notebook.ipynb) to see the evaluation process.
+<br></br>
+![Markdown symbol](https://github.com/MBMMurad/asl-2d-to-3d/blob/master/Screenshot%20from%202023-07-13%2023-11-59.png)
+
+# How to use it?
+[This Notebook](https://github.com/MBMMurad/Bangla-Person-Name-Extractor/blob/main/Inference_template.ipynb) contains the required Inference Template on a sentence.
+<br></br>
+You can also directly infer using the following code snippet. Just change the sentence.
+
+```
+from transformers import AutoModelForPreTraining, AutoTokenizer,AutoModelForTokenClassification #!pip install transformers==4.30.2
+from normalizer import normalize #pip install git+https://github.com/csebuetnlp/normalizer
+import torch #pip install torch
+import numpy as np #!pip install numpy==1.23.5
+
+
+model = AutoModelForTokenClassification.from_pretrained("MBMMurad/BanglaBERT_Person_Name_Extractor")
+tokenizer = AutoTokenizer.from_pretrained("MBMMurad/BanglaBERT_Person_Name_Extractor")
+def inference_fn(sentence):
+    sentence = normalize(sentence)
+    tokens = tokenizer.tokenize(sentence)
+    inputs = tokenizer.encode(sentence,return_tensors="pt")
+    outputs = model(inputs).logits
+    predictions = torch.argmax(outputs[0],axis=1)[1:-1].numpy()
+    idxs = np.where(predictions==1)
+
+    return np.array(tokens)[idxs]
+
+sentence = "আব্দুর রহিম নামের কাস্টমারকে একশ টাকা বাকি দিলাম।"
+pred = inference_fn(sentence)
+print(f"Input Sentence : {sentence}")
+print(f"Person Name Entities : {pred}")
+
+sentence = "ইঞ্জিনিয়ার্স ইনস্টিটিউশন চট্টগ্রামের সাবেক সভাপতি প্রকৌশলী দেলোয়ার হোসেন মজুমদার প্রথম আলোকে বলেন, 'সংকট নিরসনে বর্তমান খালগুলোকে পূর্ণ প্রবাহে ফিরিয়ে আনার পাশাপাশি নতুন তিনটি খাল খনন জরুরি।'"
+pred = inference_fn(sentence)
+print(f"Input Sentence : {sentence}")
+print(f"Person Name Entities : {pred}")
+
+
+sentence = "দলীয় নেতারা তাঁর বাসভবনে যেতে চাইলে আটক হন।"
+pred = inference_fn(sentence)
+print(f"Input Sentence : {sentence}")
+print(f"Person Name Entities : {pred}")
+```
+
+**Output :** 
+``` 
+Input Sentence : আব্দুর রহিম নামের কাস্টমারকে একশ টাকা বাকি দিলাম।
+Person Name Entities : ['আব্দুর' 'রহিম']
+
+
+Input Sentence : ইঞ্জিনিয়ার্স ইনস্টিটিউশন চট্টগ্রামের সাবেক সভাপতি প্রকৌশলী দেলোয়ার হোসেন মজুমদার প্রথম আলোকে বলেন, 'সংকট নিরসনে বর্তমান খালগুলোকে পূর্ণ প্রবাহে ফিরিয়ে আনার পাশাপাশি নতুন তিনটি খাল খনন জরুরি।'
+Person Name Entities : ['দেলোয়ার' 'হোসেন' 'মজুমদার']
+
+
+Input Sentence : দলীয় নেতারা তাঁর বাসভবনে যেতে চাইলে আটক হন।
+Person Name Entities : []
+```