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FA4-0 / Leveraging-Pre-trained-Models-for-Failure-Analysis-Triplets-Generation

FATG: Leveraging Pre-trained Models for Failure Analysis Triplets Generation

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Leveraging-Pre-trained-Models-for-Failure-Analysis-Triplets-Generation

ABSTRACT

Pre-trained Language Models recently gained traction in the Natural Language Processing (NLP)
domain for text summarization, generation and question answering tasks. This stems from the
innovation introduced in Transformer models and their overwhelming performance compared with
Recurrent Neural Network Models (Long Short Term Memory (LSTM)). In this paper, we leverage
the attention mechanism of pre-trained causal language models such as Transformer model for
the downstream task of generating Failure Analysis Triplets (FATs) - a sequence of steps for
analyzing defected components in the semiconductor industry. We compare different transformer
model for this generative task and observe that Generative Pre-trained Transformer 2 (GPT2)
outperformed other transformer model for the failure analysis triplet generation (FATG) task.
In particular, we observe that GPT2 (trained on 1.5B parameters) outperforms pre-trained 
BERT, BART and GPT3 by a large margin on ROUGE. Furthermore, we introduce LEvenshstein
Sequential Evaluation metric (LESE) for better evaluation of the structured FAT data and
show that it compares exactly with human judgment than existing metrics.

How to use

  • Clone repository: git clone https://github.com/AI-for-Fault-Analysis-FA4-0/Leveraging-Pre-trained-Models-for-Failure-Analysis-Triplets-Generation
  • Runing training and evaluation example 
    •      python pretrainer.py \
     --model_type gpt2 \
     --model_name_or_path gpt2 \
     --do_train \
     --do_eval \
     --max_seq_length 128 \
     --per_gpu_train_batch_size 1 \
     --learning_rate 5e-5 \
     --num_train_epochs 5.0 \
     --output_dir result/ \
     --eval_dir evaluation/ \
     --overwrite_output_dir \
     --fp16 \
     --fp16_opt_level O2 \
     --gradient_accumulation_steps 1 \
     --seed 42 \
     --do_lower_case \
     --warmup_steps 100 \
     --logging_steps 100 \
     --save_steps 100 \
     --evaluate_during_training \
     --save_total_limit 1 \
     --adam_epsilon 1e-8 \
     --weight_decay 0.05 \
     --max_grad_norm 1.0 \
     --return_token_type_ids \
     #--use_weights \
     --max_steps -1
     ```
  • Model type/name with Causal LMHead
    • facebook/bart-large-cnn: Bidirectional Auto-Regressive Transformer
    • bert-base-uncased: Bidirectional Encoder Representations from Transformers
    • roberta-large: Robustly Optimized BERT Pretraining Approach
    • distilbert-base-uncased: A distilled version of BERT: smaller, faster, cheaper and lighter
    • xlnet-large-cased: Generalized Autoregressive Pretraining for Language Understanding
    • openai-gpt: Generative Pre-trained Transformer 3
    • gpt2: Generative Pre-trained Transformer 2 (base)
    • gpt2-medium: Generative Pre-trained Transformer 2 (Medium)
    • gpt2-large: Generative Pre-trained Transformer 2 (Large)

Results

Cross-entropy loss & co.

Model BLEU-1 BLEU-3 MET. ROUGE-1 ROUGE-L LESE-1 Lev-1 LESE-3 Lev-3 PPL
Prec. Rec. F1 Prec. Rec. F1 Prec. Rec. F1 Prec. Rec. F1
BART 3.04 1.67 3.23 - - - - - - 2.12 3.85 2.28 73.0 0.01 0.0 0.0 24.0 1.0
BERT 1.81 0.65 4.0 6.7 12.0 7.99 5.62 10.21 6.72 1.38 10.48 2.33 287.0 0.02 0.03 0.01 96.0 1.0
ROBERTA 0.14 0.11 0.32 0.26 0.56 0.34 0.26 0.55 0.34 0.09 0.33 0.13 169.0 0.0 0.0 0.0 56.0 1.0
GPT3 22.71 16.6 29.34 30.06 35.75 30.26 27.65 32.93 27.83 20.88 24.93 20.64 45.0 9.34 11.28 9.29 16.0 1.53
GPT2-B 20.85 15.25 25.67 30.66 31.86 28.78 28.1 29.2 26.35 21.31 21.1 19.17 41.0 9.31 9.3 8.39 15.0 1.42
GPT2-M 21.26 15.47 26.74 30.37 33.28 29.15 27.65 30.4 26.56 21.08 22.06 19.41 43.0 9.23 9.79 8.55 15.0 1.52
GPT2-L 22.87 16.87 28.7 31.88 35.19 30.87 29.19 32.24 28.24 22.01 23.83 20.81 42.0 10.06 10.89 9.53 15.0 1.412
W_BART 4.71 1.87 5.04 5.41 11.05 6.57 4.36 8.97 5.28 2.56 5.9 3.17 81.0 0.0 0.0 0.0 27.0 1.01
W_BERT 0.42 0.2 0.56 1.08 1.49 1.15 0.97 1.37 1.04 0.33 1.15 0.44 74.0 0.01 0.0 0.0 24.0 1.0
W_ROBERTA 0.07 0.06 0.33 0.11 0.32 0.16 0.11 0.31 0.15 0.05 0.2 0.07 196.0 0.0 0.0 0.0 65.0 1.0
W_GPT3 - - - - - - - - - - - - - - - - - 1.34
W_GPT2-B 21.15 15.52 26.21 31.27 32.49 29.33 28.51 29.64 26.72 21.64 21.52 19.49 41.0 9.59 9.62 8.65 15.0 1.28
W_GPT2-M 20.99 15.38 26.34 30.05 32.53 28.68 27.44 29.76 26.2 21.0 21.68 19.28 42.0 9.43 9.75 8.67 15.0 1.34
W_GPT2-L 22.67 16.64 28.68 31.66 35.54 30.89 29.02 32.54 28.26 21.93 24.09 20.8 43.0 10.1 11.17 9.62 16.0 1.26

Variational Loss

Model BLEU-1 BLEU-3 MET. ROUGE-1 ROUGE-L LESE-1 Lev-1 LESE-3 Lev-3 PPL
Prec. Rec. F1 Prec. Rec. F1 Prec. Rec. F1 Prec. Rec. F1
BETAVAE_MAH_BART 1.96 1.37 1.7 4.86 3.4 3.63 4.56 3.19 3.39 1.41 1.95 1.47 54.0 0.0 0.0 0.0 18.0 1.440000057220459
BETAVAE_MAH_BERT 9.91 3.56 8.78 27.17 21.61 20.77 23.24 18.49 17.7 10.4 9.01 7.53 49.0 0.94 0.37 0.36 17.0 1.9900000095367432
BETAVAE_MAH_ROBERTA 0.34 0.2 0.53 0.66 1.35 0.81 0.61 1.24 0.75 0.24 1.03 0.35 265.0 0.0 0.0 0.0 88.0 1.940000057220459
BETAVAE_MAH_GPT2-B 15.18 5.53 12.17 21.7 22.05 20.09 19.28 19.66 17.85 11.98 12.17 10.83 45.0 0.29 0.33 0.27 16.0 1.9700000286102295
BETAVAE_MAH_GPT2-M 14.52 5.14 12.19 18.74 22.03 18.52 16.52 19.48 16.32 10.26 12.71 10.18 50.0 0.3 0.41 0.31 18.0 2.049999952316284
BETAVAE_MAH_GPT2-L 13.95 5.0 11.68 18.55 21.8 18.23 16.32 19.24 16.05 10.56 12.27 10.06 50.0 0.27 0.38 0.28 18.0 1.940000057220459
GCVAE_MMD_BART 4.66 1.8 4.31 6.76 12.74 7.5 5.03 9.11 5.35 2.94 4.3 2.82 57.0 0.16 0.01 0.03 19.0 1.7799999713897705
GCVAE_MMD_BERT 9.54 3.32 8.51 21.61 20.21 18.31 18.29 17.25 15.5 8.16 9.14 6.87 54.0 0.42 0.18 0.16 19.0 2.4600000381469727
GCVAE_MMD_ROBERTA 1.85 0.86 2.43 3.82 6.44 4.17 3.29 5.61 3.59 1.58 4.28 1.84 143.0 0.07 0.01 0.01 48.0 2.450000047683716
GCVAE_MMD_GPT2-B 15.12 5.21 11.65 21.42 21.43 19.69 18.88 18.99 17.38 11.61 11.83 10.56 44.0 0.23 0.26 0.22 16.0 2.5299999713897705
GCVAE_MMD_GPT2-M 14.44 4.79 12.09 18.66 22.74 18.7 16.4 20.08 16.44 10.02 13.5 10.21 54.0 0.18 0.29 0.2 19.0 2.5899999141693115
GCVAE_MMD_GPT2-L 13.95 4.45 12.56 17.5 24.57 18.52 15.17 21.37 16.06 9.37 14.86 10.12 62.0 0.16 0.23 0.17 22.0 2.4000000953674316
GCVAE_MAH_BART 4.22 1.79 3.58 6.91 10.26 6.96 5.24 7.45 5.08 2.82 3.41 2.53 50.0 0.09 0.01 0.02 17.0 1.7799999713897705
GCVAE_MAH_BERT 9.35 3.38 7.5 23.8 17.39 17.99 19.74 14.73 15.04 8.32 7.94 6.69 47.0 0.45 0.13 0.14 16.0 2.4600000381469727
GCVAE_MAH_ROBERTA 1.18 0.64 1.7 1.18 3.68 1.72 1.06 3.33 1.54 0.78 2.85 1.16 142.0 0.01 0.04 0.01 47.0 2.4600000381469727
GCVAE_MAH_GPT2-B 15.7 5.36 12.18 21.82 22.24 20.29 19.23 19.72 17.91 11.68 12.34 10.81 45.0 0.25 0.28 0.23 16.0 2.5299999713897705
GCVAE_MAH_GPT2-M 14.34 4.81 11.89 19.06 22.28 18.75 16.7 19.66 16.46 10.32 13.1 10.2 52.0 0.19 0.27 0.2 19.0 2.5799999237060547
GCVAE_MAH_GPT2-L 14.05 4.75 11.53 18.59 21.96 18.38 16.19 19.23 16.03 10.13 12.44 9.94 51.0 0.2 0.26 0.2 18.0 2.390000104904175
CONTROLVAE_MAH_BART 4.66 1.98 3.6 7.42 9.69 7.42 5.48 6.94 5.29 2.57 2.57 2.09 47.0 0.21 0.02 0.04 16.0 1.7799999713897705
CONTROLVAE_MAH_BERT 7.91 2.91 7.19 18.97 17.7 15.82 15.88 15.11 13.3 7.13 8.4 5.97 57.0 0.32 0.17 0.13 20.0 2.4600000381469727
CONTROLVAE_MAH_ROBERTA 1.17 0.52 1.96 1.38 4.7 2.04 1.2 4.17 1.78 0.87 4.24 1.38 200.0 0.0 0.01 0.0 67.0 2.450000047683716
CONTROLVAE_MAH_GPT2-B 15.83 5.34 12.43 21.97 22.66 20.51 19.34 20.06 18.09 11.6 12.75 10.92 46.0 0.23 0.3 0.23 17.0 2.5399999618530273
CONTROLVAE_MAH_GPT2-M 14.56 4.78 12.24 18.94 23.02 18.95 16.61 20.29 16.64 10.34 13.68 10.43 53.0 0.17 0.26 0.19 19.0 2.5799999237060547
CONTROLVAE_MAH_GPT2-L 13.77 4.72 11.22 18.43 21.1 17.91 16.09 18.46 15.64 10.16 11.86 9.67 50.0 0.19 0.21 0.17 18.0 2.390000104904175
VAE_MAH_BART 2.8 1.5 3.89 29.29 9.75 13.63 22.94 7.76 10.71 19.24 4.41 6.77 38.0 3.11 0.35 0.61 13.0 4.869999885559082
VAE_MAH_BERT 0.15 0.08 0.4 34.72 5.42 8.48 30.04 4.32 6.82 5.31 0.51 0.87 39.0 inf 0.18 nan 13.0 6.679999828338623
VAE_MAH_ROBERTA 2.34 1.0 2.67 7.64 7.51 5.94 6.94 6.98 5.44 3.36 5.19 2.7 79.0 0.38 0.06 0.08 27.0 7.0
VAE_MAH_GPT2-B 16.27 5.52 11.65 33.56 19.81 23.16 27.29 16.48 19.06 15.56 10.45 11.47 38.0 0.17 0.11 0.12 14.0 9.010000228881836
VAE_MAH_GPT2-M 14.33 4.74 11.0 29.92 19.76 21.23 24.71 16.69 17.75 15.92 11.21 11.37 42.0 0.25 0.12 0.12 15.0 7.579999923706055
VAE_MAH_GPT2-L 14.25 4.6 10.74 21.72 19.39 18.69 18.19 16.45 15.73 10.56 11.3 9.68 47.0 0.09 0.11 0.08 17.0 6.019999980926514

Cite

@misc{https://doi.org/10.48550/arxiv.2210.17497,
  doi = {10.48550/ARXIV.2210.17497},
  url = {https://arxiv.org/abs/2210.17497},
  author = {Ezukwoke, Kenneth and Hoayek, Anis and Batton-Hubert, Mireille and Boucher, Xavier and Gounet, Pascal and Adrian, Jerome},
  keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), Applications (stat.AP), FOS: Computer and information sciences, FOS: Computer and information sciences, G.3; I.2; I.7, 68Txx, 68Uxx},
  title = {Leveraging Pre-trained Models for Failure Analysis Triplets Generation},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

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FATG: Leveraging Pre-trained Models for Failure Analysis Triplets Generation

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