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48
api.py
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48
api.py
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import torch
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import uvicorn
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from fastapi import FastAPI
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from pydantic import BaseModel
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from transformers import MT5Tokenizer,AutoTokenizer, AutoModel ,T5ForConditionalGeneration
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import warnings
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import json
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import random
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import torch.nn.functional as F
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#from ece import compute_ECE
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from torch.utils.data import DataLoader
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from functools import reduce
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warnings.filterwarnings("ignore")
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DEVICE ='cpu'
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model_dir = "C:/Users/david/Desktop/T5_JUPYTER/qa_model"
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tokenizer_dir = "C:/Users/david/Desktop/T5_JUPYTER/qa_tokenizer"
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MODEL = T5ForConditionalGeneration.from_pretrained(model_dir, from_tf=False, return_dict=True).to(DEVICE)
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print("Model succesfully loaded!")
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TOKENIZER = AutoTokenizer.from_pretrained(tokenizer_dir, use_fast=True)
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print("Tokenizer succesfully loaded!")
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Q_LEN = 512
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TOKENIZER.add_tokens('<sep>')
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print('model loaded')
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app = FastAPI()
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# BASE MODEL
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class InputData(BaseModel):
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context: str
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question: str
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@app.post("/predict")
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async def predict(input_data: InputData):
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inputs = TOKENIZER(input_data.question, input_data.context, max_length=512, padding="max_length", truncation=True, add_special_tokens=True)
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input_ids = torch.tensor(inputs["input_ids"], dtype=torch.long).to(DEVICE).unsqueeze(0)
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attention_mask = torch.tensor(inputs["attention_mask"], dtype=torch.long).to(DEVICE).unsqueeze(0)
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outputs = MODEL.generate(input_ids=input_ids, attention_mask=attention_mask, return_dict_in_generate=True,output_scores=True,max_length=512)
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predicted_ids = outputs.sequences.numpy()
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predicted_text = TOKENIZER.decode(predicted_ids[0], skip_special_tokens=True)
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return {'prediction':predicted_text}
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if __name__ == "__main__":
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uvicorn.run(app, host="127.0.0.1", port=8090)
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33
aplication.py
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33
aplication.py
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import requests
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import json
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import streamlit as st
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def predict(context,question):
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url = 'http://localhost:8090/predict'
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data = {'context': context,'question': question}
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json_data = json.dumps(data)
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headers = {'Content-type': 'application/json'}
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response = requests.post(url, data=json_data, headers=headers)
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result = response.json()
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return result
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def main():
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st.title("T5 model inference")
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# Vytvoríme polia pre zadanie hodnôt
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context = st.text_input("context:")
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question = st.text_input("question:")
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prediction = predict(context,question)
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# Vytvoríme tlačidlo pre vykonanie akcie
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if st.button("Execute"):
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st.json({
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'context': context,
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'question': question,
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'prediciton':prediction
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})
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if __name__ == "__main__":
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main()
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91
condadiplo.yaml
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91
condadiplo.yaml
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name: DIPLOcondaEnviroment
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channels:
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- defaults
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dependencies:
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- bzip2=1.0.8=he774522_0
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- ca-certificates=2023.01.10=haa95532_0
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- libffi=3.4.2=hd77b12b_6
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- openssl=1.1.1t=h2bbff1b_0
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- pip=23.0.1=py311haa95532_0
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- python=3.11.2=h966fe2a_0
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- setuptools=66.0.0=py311haa95532_0
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- sqlite=3.41.2=h2bbff1b_0
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- tk=8.6.12=h2bbff1b_0
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- vc=14.2=h21ff451_1
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- vs2015_runtime=14.27.29016=h5e58377_2
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- wheel=0.38.4=py311haa95532_0
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- xz=5.2.10=h8cc25b3_1
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- zlib=1.2.13=h8cc25b3_0
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- pip:
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- altair==4.2.2
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- anyio==3.6.2
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- attrs==23.1.0
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- blinker==1.6.2
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- cachetools==5.3.0
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- certifi==2022.12.7
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- charset-normalizer==3.1.0
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- click==8.1.3
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- colorama==0.4.6
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- comtypes==1.1.14
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- decorator==5.1.1
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- entrypoints==0.4
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- fastapi==0.92.0
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- filelock==3.11.0
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- gitdb==4.0.10
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- gitpython==3.1.31
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- h11==0.14.0
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- huggingface-hub==0.12.1
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- idna==3.4
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- importlib-metadata==6.4.1
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- jinja2==3.1.2
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- jsonschema==4.17.3
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- markdown-it-py==2.2.0
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- markupsafe==2.1.2
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- mdurl==0.1.2
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- mouseinfo==0.1.3
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- mpmath==1.3.0
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- networkx==3.1
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- numpy==1.24.2
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- packaging==23.1
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- pandas==1.5.3
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- pillow==9.5.0
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- protobuf==3.20.3
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- pyarrow==11.0.0
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- pydantic==1.10.7
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- pydeck==0.8.1b0
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- pygments==2.15.0
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- pympler==1.0.1
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- pyperclip==1.8.2
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- pyrsistent==0.19.3
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- python-dateutil==2.8.2
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- pytz==2023.3
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- pytz-deprecation-shim==0.1.0.post0
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- pywin32==305
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- pywinauto==0.6.8
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- pyyaml==6.0
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- regex==2023.3.23
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- requests==2.28.2
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- rich==13.3.4
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- six==1.16.0
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- smmap==5.0.0
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- sniffio==1.3.0
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- starlette==0.25.0
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- streamlit==1.21.0
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- sympy==1.11.1
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- tokenizers==0.13.2
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- toml==0.10.2
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- toolz==0.12.0
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- torch==2.0.0
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- torchaudio==2.0.1
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- torchvision==0.15.1
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- tornado==6.2
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- tqdm==4.65.0
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- transformers==4.26.1
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- typing-extensions==4.5.0
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- tzdata==2023.3
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- tzlocal==4.3
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- urllib3==1.26.15
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- uvicorn==0.20.0
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- validators==0.20.0
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- watchdog==3.0.0
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- zipp==3.15.0
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72
requirements.txt
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72
requirements.txt
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altair==4.2.2
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anyio==3.6.2
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attrs==23.1.0
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blinker==1.6.2
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cachetools==5.3.0
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certifi==2022.12.7
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charset-normalizer==3.1.0
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click==8.1.3
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colorama==0.4.6
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comtypes==1.1.14
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decorator==5.1.1
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entrypoints==0.4
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fastapi==0.92.0
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filelock==3.11.0
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gitdb==4.0.10
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GitPython==3.1.31
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h11==0.14.0
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huggingface-hub==0.12.1
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idna==3.4
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importlib-metadata==6.4.1
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Jinja2==3.1.2
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jsonschema==4.17.3
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markdown-it-py==2.2.0
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MarkupSafe==2.1.2
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mdurl==0.1.2
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MouseInfo==0.1.3
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mpmath==1.3.0
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networkx==3.1
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numpy==1.24.2
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packaging==23.1
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pandas==1.5.3
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Pillow==9.5.0
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protobuf==3.20.3
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pyarrow==11.0.0
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pydantic==1.10.7
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pydeck==0.8.1b0
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Pygments==2.15.0
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Pympler==1.0.1
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pyperclip==1.8.2
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pyrsistent==0.19.3
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python-dateutil==2.8.2
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pytz==2023.3
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pytz-deprecation-shim==0.1.0.post0
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pywin32==305
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pywinauto==0.6.8
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PyYAML==6.0
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regex==2023.3.23
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requests==2.28.2
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rich==13.3.4
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six==1.16.0
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smmap==5.0.0
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sniffio==1.3.0
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starlette==0.25.0
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streamlit==1.21.0
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sympy==1.11.1
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tokenizers==0.13.2
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toml==0.10.2
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toolz==0.12.0
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torch==2.0.0
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torchaudio==2.0.1
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torchvision==0.15.1
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tornado==6.2
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tqdm==4.65.0
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transformers==4.26.1
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typing_extensions==4.5.0
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tzdata==2023.3
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tzlocal==4.3
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urllib3==1.26.15
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uvicorn==0.20.0
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validators==0.20.0
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watchdog==3.0.0
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zipp==3.15.0
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175
train.py
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train.py
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import torch
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import json
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from tqdm import tqdm
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import torch.nn as nn
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from torch.optim import Adam
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import nltk
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import spacy
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import string
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import evaluate # Bleu
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from torch.utils.data import Dataset, DataLoader, RandomSampler
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import pandas as pd
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import numpy as np
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import transformers
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from sklearn.model_selection import train_test_split
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import matplotlib.pyplot as plt
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#from transformers import T5Tokenizer, T5Model, T5ForConditionalGeneration, T5TokenizerFast
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from transformers import AutoTokenizer, T5ForConditionalGeneration
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import warnings
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warnings.filterwarnings("ignore")
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print("Imports succesfully done")
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DEVICE ='cuda:0'
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#TOKENIZER = AutoTokenizer.from_pretrained("ApoTro/slovak-t5-small")
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TOKENIZER=AutoTokenizer.from_pretrained('google/mt5-small')
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#TOKENIZER=AutoTokenizer.from_pretrained('google/mt5-base')
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TOKENIZER.add_tokens('<sep>')
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#MODEL = T5ForConditionalGeneration.from_pretrained("ApoTro/slovak-t5-small").to(DEVICE)
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MODEL = T5ForConditionalGeneration.from_pretrained("google/mt5-small").to(DEVICE)
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#MODEL = T5ForConditionalGeneration.from_pretrained("google/mt5-base").to(DEVICE)
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#pridam token
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MODEL.resize_token_embeddings(len(TOKENIZER))
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#lr = learning rate = 10-5
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OPTIMIZER = Adam(MODEL.parameters(), lr=0.00001)
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Q_LEN = 256 # Question Length
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T_LEN = 32 # Target Length
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BATCH_SIZE = 4 #dávka dát
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print("Model succesfully loaded")
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path_train = '/home/omasta/T5_JUPYTER/skquad-221017/train-v1.json'
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with open(path_train) as f:
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data = json.load(f)
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def prepare_data(data):
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articles = []
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for article in data["data"]:
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for paragraph in article["paragraphs"]:
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for qa in paragraph["qas"]:
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question = qa["question"]
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answer = qa["answers"][0]["text"]
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#input_ = 'generuj_odpoved : ' + paragraph["context"] + ' <sep>' + question + ' <sep>'
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inputs = {"input": paragraph["context"]+'<sep>'+question, "answer": answer}
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#inputs = {'context': input_ ,'answer':answer}
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articles.append(inputs)
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return articles
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prepared_data = prepare_data(data)
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print(prepared_data[0])
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#Dataframe
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data = pd.DataFrame(prepared_data)
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class QA_Dataset(Dataset):
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def __init__(self, tokenizer, dataframe, q_len, t_len):
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self.tokenizer = tokenizer
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self.q_len = q_len
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self.t_len = t_len
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self.data = dataframe
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self.input = self.data['input']
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#self.context = self.data["context"]
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self.answer = self.data['answer']
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def __len__(self):
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return len(self.questions)
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def __getitem__(self, idx):
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input = self.input[idx]
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answer = self.answer[idx]
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input_tokenized = self.tokenizer(input, max_length=self.q_len, padding="max_length",
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truncation=True, pad_to_max_length=True, add_special_tokens=True)
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answer_tokenized = self.tokenizer(answer, max_length=self.t_len, padding="max_length",
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truncation=True, pad_to_max_length=True, add_special_tokens=True)
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labels = torch.tensor(answer_tokenized["input_ids"], dtype=torch.long)
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labels[labels == 0] = -100
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return {
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"input_ids": torch.tensor(input_tokenized["input_ids"], dtype=torch.long),
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"attention_mask": torch.tensor(input_tokenized["attention_mask"], dtype=torch.long),
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"labels": labels,
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"decoder_attention_mask": torch.tensor(answer_tokenized["attention_mask"], dtype=torch.long)
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}
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##DATA LOADERS
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train_data, val_data = train_test_split(data, test_size=0.2, random_state=42)
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train_sampler = RandomSampler(train_data.index)
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val_sampler = RandomSampler(val_data.index)
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qa_dataset = QA_Dataset(TOKENIZER, data, Q_LEN, T_LEN)
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train_loader = DataLoader(qa_dataset, batch_size=BATCH_SIZE, sampler=train_sampler)
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val_loader = DataLoader(qa_dataset, batch_size=BATCH_SIZE, sampler=val_sampler)
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print("Loaders working fine")
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### TRAINING (46MINS ACCORDING THE V1_DATA)
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train_loss = 0
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val_loss = 0
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train_batch_count = 0
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val_batch_count = 0
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for epoch in range(4):
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MODEL.train()
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for batch in tqdm(train_loader, desc="Training batches"):
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input_ids = batch["input_ids"].to(DEVICE)
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attention_mask = batch["attention_mask"].to(DEVICE)
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labels = batch["labels"].to(DEVICE)
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decoder_attention_mask = batch["decoder_attention_mask"].to(DEVICE)
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outputs = MODEL(
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input_ids=input_ids,
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attention_mask=attention_mask,
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labels=labels,
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decoder_attention_mask=decoder_attention_mask
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)
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OPTIMIZER.zero_grad()
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outputs.loss.backward()
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OPTIMIZER.step()
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train_loss += outputs.loss.item()
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train_batch_count += 1
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#Evaluation
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MODEL.eval()
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for batch in tqdm(val_loader, desc="Validation batches"):
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input_ids = batch["input_ids"].to(DEVICE)
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attention_mask = batch["attention_mask"].to(DEVICE)
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labels = batch["labels"].to(DEVICE)
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decoder_attention_mask = batch["decoder_attention_mask"].to(DEVICE)
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outputs = MODEL(
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input_ids=input_ids,
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attention_mask=attention_mask,
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labels=labels,
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decoder_attention_mask=decoder_attention_mask
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)
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OPTIMIZER.zero_grad()
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outputs.loss.backward()
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OPTIMIZER.step()
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val_loss += outputs.loss.item()
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val_batch_count += 1
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print(f"{epoch+1}/{2} -> Train loss: {train_loss / train_batch_count}\tValidation loss: {val_loss/val_batch_count}")
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print("Training done succesfully")
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## SAVE FINE_TUNED MODEL
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MODEL.save_pretrained("qa_model_mT5_small")
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TOKENIZER.save_pretrained('qa_tokenizer_mT5_small')
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139
usecase.py
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139
usecase.py
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## IMPORT NESSESARY EQUIPMENTS
|
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from transformers import T5ForConditionalGeneration, T5Tokenizer,AutoTokenizer
|
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import torch
|
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import evaluate # Bleu
|
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import json
|
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import random
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import statistics
|
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from sklearn.metrics import precision_score, recall_score, f1_score
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## TURN WARNINGS OFF
|
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import warnings
|
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warnings.filterwarnings("ignore")
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##13/03/23 added
|
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from rouge import Rouge
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|
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# Názov modelu
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DEVICE ='cuda:0'
|
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|
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|
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#T5 MODEL
|
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#model_name = 'T5_SK_model'
|
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#model_dir = "/home/omasta/T5_JUPYTER/qa_model"
|
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#tokenizer_dir = "/home/omasta/T5_JUPYTER/qa_tokenizer"
|
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|
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#mT5 SMALL MODEL
|
||||
model_name = 'mT5_SMALL'
|
||||
model_dir = '/home/omasta/T5_JUPYTER/qa_model_mT5_small'
|
||||
tokenizer_dir = '/home/omasta/T5_JUPYTER/qa_tokenizer_mT5_small'
|
||||
|
||||
#Načítanie modelu z adresára
|
||||
MODEL = T5ForConditionalGeneration.from_pretrained(model_dir, from_tf=False, return_dict=True).to(DEVICE)
|
||||
print("Model succesfully loaded!")
|
||||
TOKENIZER = AutoTokenizer.from_pretrained(tokenizer_dir, use_fast=True)
|
||||
print("Tokenizer succesfully loaded!")
|
||||
Q_LEN = 512
|
||||
TOKENIZER.add_tokens('<sep>')
|
||||
MODEL.resize_token_embeddings(len(TOKENIZER))
|
||||
|
||||
def predict_answer(data, ref_answer=None,random=None):
|
||||
predictions=[]
|
||||
for i in data:
|
||||
inputs = TOKENIZER(i['input'], max_length=Q_LEN, padding="max_length", truncation=True, add_special_tokens=True)
|
||||
input_ids = torch.tensor(inputs["input_ids"], dtype=torch.long).to(DEVICE).unsqueeze(0)
|
||||
attention_mask = torch.tensor(inputs["attention_mask"], dtype=torch.long).to(DEVICE).unsqueeze(0)
|
||||
outputs = MODEL.generate(input_ids=input_ids, attention_mask=attention_mask)
|
||||
predicted_answer = TOKENIZER.decode(outputs.flatten(), skip_special_tokens=True)
|
||||
ref_answer = i['answer'].lower()
|
||||
#print(ref_answer)
|
||||
if ref_answer:
|
||||
# Load the Bleu metric
|
||||
bleu = evaluate.load("google_bleu")
|
||||
#print('debug')
|
||||
#precision = list(precision_score(ref_answer, predicted_answer))
|
||||
#recall = list(recall_score(ref_answer, predicted_answer))
|
||||
#f1 = list(f1_score(ref_answer, predicted_answer))
|
||||
score = bleu.compute(predictions=[predicted_answer],
|
||||
references=[ref_answer])
|
||||
predictions.append({'prediction':predicted_answer,'ref_answer':ref_answer,'score':score['google_bleu']})
|
||||
return predictions
|
||||
|
||||
def prepare_data(data):
|
||||
articles = []
|
||||
for article in data["data"]:
|
||||
for paragraph in article["paragraphs"]:
|
||||
for qa in paragraph["qas"]:
|
||||
question = qa["question"]
|
||||
answer = qa["answers"][0]["text"]
|
||||
inputs = {"input": paragraph["context"]+ "<sep>" + question, "answer": answer}
|
||||
articles.append(inputs)
|
||||
|
||||
return articles
|
||||
|
||||
dev_data_path = '/home/omasta/T5_JUPYTER/skquad-221017/dev-v1.json'
|
||||
with open(dev_data_path,'r') as f:
|
||||
data=json.load(f)
|
||||
#print('data imported')
|
||||
|
||||
dev_data = prepare_data(data)
|
||||
|
||||
#print('data prepared')
|
||||
print(f'Number of dev samples {len(dev_data)}')
|
||||
print(dev_data[0])
|
||||
bleu_score = []
|
||||
precisions=[]
|
||||
f1_scores=[]
|
||||
recall_scores=[]
|
||||
rouge_1 = []
|
||||
rouge_2 = []
|
||||
#X = 150
|
||||
evaluate = predict_answer(dev_data)
|
||||
rouge = Rouge()
|
||||
for item in evaluate:
|
||||
bleu_score.append(item['score'])
|
||||
try:
|
||||
#scores = rouge.get_scores(item['prediction'], item['ref_answer'], avg=True)
|
||||
precision=precision_score(list(item['ref_answer']), list(item['prediction']),average='macro')
|
||||
recall=recall_score(list(item['ref_answer']), list(item['prediction']),average='macro')
|
||||
f1=f1_score(list(item['ref_answer']), list(item['prediction']),average='macro')
|
||||
except ValueError:
|
||||
precision=0
|
||||
recall=0
|
||||
f1=0
|
||||
precisions.append(precision)
|
||||
f1_scores.append(f1)
|
||||
recall_scores.append(recall)
|
||||
|
||||
|
||||
def rouge_eval(dict_x):
|
||||
rouge = Rouge()
|
||||
rouge_scores=[]
|
||||
for item in dict_x:
|
||||
if item['prediction'] and item['ref_answer']:
|
||||
rouge_score = rouge.get_scores(item['prediction'], item['ref_answer'])
|
||||
rouge_scores.append(rouge_score)
|
||||
else:
|
||||
continue
|
||||
return rouge_scores
|
||||
|
||||
|
||||
print(f'VYHODNOTENIE VYSLEDKOV : ------------------------')
|
||||
#print(evaluate)
|
||||
#bleu_score_total = statistics.mean(bleu_score)
|
||||
#recall_score_total= statistics.mean(recall_scores)
|
||||
#f1_score_total = statistics.mean(f1_scores)
|
||||
#precision_total = statistics.mean(precisions)
|
||||
#print(f'Bleu_score of model {model_name} : ',bleu_score_total)
|
||||
#print(f'Recall of model {model_name}: ',recall_score_total)
|
||||
#print(f'F1 of model {model_name} : ', f1_score_total)
|
||||
#print(f'Precision of model {model_name}: :',precision_total)
|
||||
#print(rouge_eval(evaluate))
|
||||
print(f'{model_name} results')
|
||||
rouge_scores = rouge_eval(evaluate)
|
||||
rouge_values = [score[0]['rouge-1']['f'] for score in rouge_scores]
|
||||
mean_rouge_score = statistics.mean(rouge_values)
|
||||
print(f'Rouge:{mean_rouge_score}')
|
||||
|
||||
rouge2_values = [score[0]['rouge-2']['f'] for score in rouge_scores]
|
||||
mean_rouge_score =statistics.mean(rouge2_values)
|
||||
print(f'Rouge-2:{mean_rouge_score}')
|
||||
|
Loading…
Reference in New Issue
Block a user