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  1. .gitattributes +35 -0
  2. README.md +13 -0
  3. app.py +55 -0
  4. requirements.txt +5 -0
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README.md ADDED
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+ ---
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+ title: YouNameIt
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+ emoji: 📉
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+ colorFrom: green
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+ colorTo: blue
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+ sdk: streamlit
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+ sdk_version: 1.26.0
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+ app_file: app.py
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+ pinned: false
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+ license: cc
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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 streamlit as st
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+ import torch
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+ from tqdm import tqdm
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+ from peft import PeftModel, PeftConfig
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+ from transformers import AutoModelForSeq2SeqLM
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+ from transformers import AutoTokenizer
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+
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+ config = PeftConfig.from_pretrained("NursNurs/T5ForReverseDictionary")
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+ model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large")
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+ model = PeftModel.from_pretrained(model, "NursNurs/T5ForReverseDictionary")
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+
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+ tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large")
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+
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+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+
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+ def return_top_k(sentence, k=10):
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+
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+ inputs = [f"Descripton : {sentence}. Word : "]
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+
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+ inputs = tokenizer(
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+ inputs,
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+ padding=True, truncation=True,
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+ return_tensors="pt",
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+ )
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+
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+
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+ model.to(device)
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+
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+ with torch.no_grad():
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+ inputs = {k: v.to(device) for k, v in inputs.items()}
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+ output_sequences = model.generate(input_ids=inputs["input_ids"], max_new_tokens=10, num_beams=k, num_return_sequences=k, #max_length=3,
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+ top_p = 50, output_scores=True, return_dict_in_generate=True) #repetition_penalty=10000.0
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+ #print("output_sequences", output_sequences)
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+ logits = output_sequences['sequences_scores'].clone().detach()
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+ decoded_probabilities = torch.softmax(logits, dim=0)
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+
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+
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+ #all word predictions
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+ predictions = [tokenizer.decode(tokens, skip_special_tokens=True) for tokens in output_sequences['sequences']]
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+ probabilities = [round(float(prob), 2) for prob in decoded_probabilities]
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+
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+ return predictions
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+
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+
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+ st.title("You name it!")
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+
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+ # adding the text that will show in the text box as default
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+ default_value = "Type the description of the word you have in mind!"
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+
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+ sent = st.text_area("Text", default_value, height = 275)
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+
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+ result = return_top_k(sent)
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+ st.write("Here are my guesses about your word:")
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+ st.write(result)
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+
requirements.txt ADDED
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+ peft==0.5.0
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+ streamlit==1.26.0
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+ torch==1.13.1
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+ tqdm==4.64.1
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+ transformers==4.33.2