AnanthZeke livinNector commited on
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Duplicate from livinNector/TaNER

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Co-authored-by: Livin nector <livinNector@users.noreply.huggingface.co>

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  1. .gitattributes +34 -0
  2. README.md +14 -0
  3. app.py +45 -0
  4. requirements.txt +5 -0
.gitattributes ADDED
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README.md ADDED
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+ ---
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+ title: TaNER
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+ emoji: 🤖
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+ colorFrom: green
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+ colorTo: blue
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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: livinNector/TaNER
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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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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+
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+ tokenizer = AutoTokenizer.from_pretrained("ai4bharat/IndicNER")
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+
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+ model = AutoModelForTokenClassification.from_pretrained("ai4bharat/IndicNER")
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+
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+
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+ def get_ner(sentence):
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+ tok_sentence = tokenizer(sentence, return_tensors='pt')
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+
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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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+
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+ predicted_labels = []
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+
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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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+
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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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+
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+
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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='NER Specialized for Tamil Language.',
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+ examples=["முதல்வர் ஸ்டாலின் பட்டமளிப்பு விழாவிற்காக சிதம்பரத்திலுள்ள அண்ணாமலைப் பல்கலைகழகத்திற்கு வருகை தந்தார்.","வல்லவராயன் வந்தியதேவனும் ஆதித்திய கரிகாலனும் கடம்பூருக்குச் சென்றனர். "], title='TaNER',
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+ article='TaNER is a model developed for NER in Tamil Language'
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+ )
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+
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+ iface.launch(enable_queue=True)
requirements.txt ADDED
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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