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lora-midm-7b-food-order-understanding – AI Model by ChloeKa | AlphaNeural AI
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lora-midm-7b-food-order-understanding
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safetensors
generated_from_trainer
K-intelligence/midm-bitext-S-7B-inst-v1
finetune
cc-by-nc-4.0
us
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<Midm 모델을 nsmc 데이터셋을 해결하는 모델이 되도록 미세튜닝 하기>
모델: Midm
데이터셋: nsmc
https://huggingface.co/datasets/nsmc
Train 데이터: 3000
Test 데이터: 1000
[테스트 결과]
정확도: 89.00%
혼동행렬(Confusion Matrix)
정답 Positive
정답 Negative
예측 Positive
474
76
예측 Negative
34
416
평가지표
정밀도(Precision)
0.862
재현율(Recall)
0.933
F1 Score
0.927
[성능 향상]
train 데이터 수를 2000에서 2500, 3000으로 늘려가며 성능을 약 8% 정도 높였으며, TrainingArguments의 max_steps 등의 파라미터를 조절해가며 성능을 높이고자 노력하였다.
lora-midm-7b-food-order-understanding
This model is a fine-tuned version of
KT-AI/midm-bitext-S-7B-inst-v1
on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 0.0001
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 2
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
training_steps: 300
mixed_precision_training: Native AMP
Training results
Framework versions
Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0
