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Duplicate from ai4bharat/IndicNER
Browse filesCo-authored-by: Umashankar <[email protected]>
- .gitattributes +34 -0
- README.md +14 -0
- app.py +45 -0
- requirements.txt +5 -0
.gitattributes
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README.md
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---
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title: IndicNER
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emoji: 📊
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colorFrom: indigo
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colorTo: yellow
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sdk: gradio
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sdk_version: 3.15.0
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app_file: app.py
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pinned: false
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license: mit
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duplicated_from: ai4bharat/IndicNER
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import gradio as gr
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import torch
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from transformers import AutoTokenizer, AutoModelForTokenClassification
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tokenizer = AutoTokenizer.from_pretrained("ai4bharat/IndicNER")
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model = AutoModelForTokenClassification.from_pretrained("ai4bharat/IndicNER")
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def get_ner(sentence):
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tok_sentence = tokenizer(sentence, return_tensors='pt')
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with torch.no_grad():
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logits = model(**tok_sentence).logits.argmax(-1)
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predicted_tokens_classes = [
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model.config.id2label[t.item()] for t in logits[0]]
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predicted_labels = []
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previous_token_id = 0
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word_ids = tok_sentence.word_ids()
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for word_index in range(len(word_ids)):
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if word_ids[word_index] == None:
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previous_token_id = word_ids[word_index]
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elif word_ids[word_index] == previous_token_id:
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previous_token_id = word_ids[word_index]
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else:
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predicted_labels.append(predicted_tokens_classes[word_index])
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previous_token_id = word_ids[word_index]
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ner_output = []
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for index in range(len(sentence.split(' '))):
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ner_output.append(
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(sentence.split(' ')[index], predicted_labels[index]))
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return ner_output
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iface = gr.Interface(get_ner,
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gr.Textbox(placeholder="Enter sentence here..."),
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["highlight"], description='The 11 languages covered by IndicNER are: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu.',
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examples=['लगातार हमलावर हो रहे शिवपाल और राजभर को सपा की दो टूक, चिट्ठी जारी कर कहा- जहां जाना चाहें जा सकते हैं', 'ಶರಣ್ ರ ನೀವು ನೋಡಲೇಬೇಕಾದ ಟಾಪ್ 5 ಕಾಮಿಡಿ ಚಲನಚಿತ್ರಗಳು'], title='IndicNER',
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article='IndicNER is a model trained to complete the task of identifying named entities from sentences in Indian languages. Our model is specifically fine-tuned to the 11 Indian languages mentioned above over millions of sentences. The model is then benchmarked over a human annotated testset and multiple other publicly available Indian NER datasets.'
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)
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iface.launch(enable_queue=True)
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requirements.txt
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transformers
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torch
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sentencepiece==0.1.95
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datasets
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seqeval
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