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TextSummarizing
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Pıshing 'Text Summarizer' portfolio project from local (cloud) to remote.

Files changed (6) hide show
  1. app.py +55 -0
  2. examples.pkl +3 -0
  3. requirements.txt +2 -0
  4. tokenizer/special_tokens_map.json +107 -0
  5. tokenizer/tokenizer.json +0 -0
  6. tokenizer/tokenizer_config.json +111 -0
app.py ADDED
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+ import gradio as gr
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+ import pickle
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+ import pickle
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+ from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM, pipeline
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+
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+ # File Paths
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+ model_path = 'fine_tuned_sum'
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+ tokenizer_path = "tokenizer"
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+ examples_path = "examples.pkl"
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+
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+ # Load the tokenizer
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+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
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+
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+ # Load the fine-tuned BERT model
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+ seq2seq_model = TFAutoModelForSeq2SeqLM.from_pretrained(model_path)
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+
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+ # loading the examples
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+ with open('examples.pkl', 'rb') as f: examples = pickle.load(f)
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+
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+ # Creating the pipeline
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+ sum_params = {
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+ "model":seq2seq_model,
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+ "tokenizer":tokenizer,
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+ "framework":"tf",
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+ }
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+
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+ summarizer = pipeline("summarization", **sum_params)
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+ # Load the model
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+ # Define a function to make predictions with the model
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+ def summarize(text):
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+
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+ # defining the params
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+ prms = {
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+ "min_length":5,
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+ "max_length":128
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+ }
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+ return summarizer(text,**prms)[0]["summary_text"]
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+
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+ # GUI Component
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+ # defining the params
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+ if_p = {
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+ "fn":summarize,
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+ "inputs":gr.inputs.Textbox(label="Text"),
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+ "outputs":gr.outputs.Textbox(label="Output"),
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+ "title":"Fine-tuned BERT moedl for text generation",
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+ "description":"Write something to generate text",
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+ "examples":examples
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+ }
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+
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+ # Create a Gradio interface instance
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+ demo = gr.Interface(**if_p)
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+
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+ # Launching the demo
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+ if __name__ == "__main__":
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+ demo.launch()
examples.pkl ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:debcffa72ef99dc95dc68f92d02ff984a2e8a0ef49aaa0939672719abce97006
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+ size 8128
requirements.txt ADDED
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+ gradio==3.27.0
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+ transformers==4.28.1
tokenizer/special_tokens_map.json ADDED
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