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ahotrod/electra_large_discriminator_squad2_512 ahotrod/electra_large_discriminator_squad2_512
119 downloads
last 30 days

pytorch

tf

Contributed by

ahotrod DNeff
4 models

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ahotrod/electra_large_discriminator_squad2_512") model = AutoModelForQuestionAnswering.from_pretrained("ahotrod/electra_large_discriminator_squad2_512")

ELECTRA_large_discriminator language model fine-tuned on SQuAD2.0

with the following results:

  "exact": 87.09677419354838,
  "f1": 89.98343832723452,
  "total": 11873,
  "HasAns_exact": 84.66599190283401,
  "HasAns_f1": 90.44759839056285,
  "HasAns_total": 5928,
  "NoAns_exact": 89.52060555088309,
  "NoAns_f1": 89.52060555088309,
  "NoAns_total": 5945,
  "best_exact": 87.09677419354838,
  "best_exact_thresh": 0.0,
  "best_f1": 89.98343832723432,
  "best_f1_thresh": 0.0

from script:

python ${EXAMPLES}/run_squad.py \
  --model_type electra \
  --model_name_or_path google/electra-large-discriminator \
  --do_train \
  --do_eval \
  --train_file ${SQUAD}/train-v2.0.json \
  --predict_file ${SQUAD}/dev-v2.0.json \
  --version_2_with_negative \
  --do_lower_case \
  --num_train_epochs 3 \
  --warmup_steps 306 \
  --weight_decay 0.01 \
  --learning_rate 3e-5 \
  --max_grad_norm 0.5 \
  --adam_epsilon 1e-6 \
  --max_seq_length 512 \
  --doc_stride 128 \
  --per_gpu_train_batch_size 8 \
  --gradient_accumulation_steps 16 \
  --per_gpu_eval_batch_size 128 \
  --fp16 \
  --fp16_opt_level O1 \
  --threads 12 \
  --logging_steps 50 \
  --save_steps 1000 \
  --overwrite_output_dir \
  --output_dir ${MODEL_PATH}

using the following system & software:

Transformers: 2.11.0
PyTorch: 1.5.0
TensorFlow: 2.2.0
Python: 3.8.1
OS/Platform: Linux-5.3.0-59-generic-x86_64-with-glibc2.10
CPU/GPU: Intel i9-9900K / NVIDIA Titan RTX 24GB