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yeye776/OndeviceAI-base

sourceHugging Facecc-by-4.0updated 3y agoView on Hugging Face
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Model Card

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OndeviceAI-base

This model is a fine-tuned version of paust/pko-t5-base on the None dataset.

How to use

python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from typing import List
 
tokenizer = AutoTokenizer.from_pretrained("yeye776/OndeviceAI-base")
model = AutoModelForSeq2SeqLM.from_pretrained("yeye776/OndeviceAI-base")
 
prompt = "분류 및 인식해줘 :"
def prepare_input(question: str):
    inputs = f"{prompt} {question}"
    input_ids = tokenizer(inputs, max_length=700, return_tensors="pt").input_ids
    return input_ids
 
def inference(question: str) -> str:
    input_data = prepare_input(question=question)
    input_data = input_data.to(model.device)
    outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=1024)
 
    result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True)
 
    return result
 
inference("안방 조명 켜줘")

Training hyperparameters

The following hyperparameters were used during training:

  • —learning_rate: 0.0007
  • —trainbatchsize: 4
  • —evalbatchsize: 4
  • —seed: 42
  • —gradientaccumulationsteps: 8
  • —totaltrainbatch_size: 32
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: cosine
  • —lrschedulerwarmup_ratio: 0.06
  • —num_epochs: 10

Training results

Framework versions

  • —Transformers 4.37.2
  • —Pytorch 2.2.0+cu121
  • —Datasets 2.16.1
  • —Tokenizers 0.15.1