deberta-v3-large-finetuned-squadv2

deberta-v3-large-finetuned-squadv2

This model is a version of microsoft/deberta-v3-large fine-tuned on the SQuAD version 2.0 dataset. Fine-tuning & evaluation on a NVIDIA Titan RTX - 24GB GPU took 15 hours.

Results from 2023 ICLR paper, "DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing", by Pengcheng He, et. al.

• 'EM' : 89.0
• 'F1' : 91.5

Results calculated with:

metrics = evaluate.load("squad_v2")
squad_v2_metrics = metrics.compute(predictions = formatted_predictions, references = references)

for this fine-tuning:

• 'exact' : 88.70,
• 'f1' : 91.52,
• 'total' : 11873,
• 'HasAns_exact' : 83.70,
• 'HasAns_f1' : 89.35,
• 'HasAns_total' : 5928,
• 'NoAns_exact' : 93.68,
• 'NoAns_f1' : 93.68,
• 'NoAns_total' : 5945,
• 'best_exact' : 88.70,
• 'best_exact_thresh' : 0.0,
• 'best_f1' : 91.52,
• 'best_f1_thresh' : 0.0}

Model description

For the authors' models, code & detailed information see: https://github.com/microsoft/DeBERTa

Intended uses

Extractive question answering on a given context

Fine-tuning hyperparameters

The following hyperparameters, as suggested by the 2023 ICLR paper noted above, were used during fine-tuning:

• learning_rate : 1e-05
• train_batch_size : 8
• eval_batch_size : 8
• seed : 42
• gradient_accumulation_steps : 8
• total_train_batch_size : 64
• optimizer : Adam with betas = (0.9, 0.999) and epsilon = 1e-06
• lr_scheduler_type : linear
• lr_scheduler_warmup_steps : 1000
• training_steps : 5200

Framework versions

• Transformers : 4.35.0.dev0
• Pytorch : 2.1.0+cu121
• Datasets : 2.14.5
• Tokenizers : 0.14.0

System

• CPU : Intel(R) Core(TM) i9-9900K - 32GB RAM
• Python version : 3.11.5 [GCC 11.2.0] (64-bit runtime)
• Python platform : Linux-5.15.0-86-generic-x86_64-with-glibc2.35
• GPU : NVIDIA TITAN RTX - 24GB Memory
• CUDA runtime version : 12.1.105
• Nvidia driver version : 535.113.01

Fine-tuning (Training) results before/after the best model (Step 3620)