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question-answering mask_token: [MASK]
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								$
								curl -X POST \
-H "Authorization: Bearer YOUR_ORG_OR_USER_API_TOKEN" \
-H "Content-Type: application/json" \
-d '{"question": "Where does she live?", "context": "She lives in Berlin."}' \
https://api-inference.huggingface.co/models/mrm8488/electra-base-finetuned-squadv1
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mrm8488/electra-base-finetuned-squadv1 mrm8488/electra-base-finetuned-squadv1
28 downloads
last 30 days

pytorch

tf

Contributed by

mrm8488 Manuel Romero
117 models

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

			
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from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mrm8488/electra-base-finetuned-squadv1") model = AutoModelForQuestionAnswering.from_pretrained("mrm8488/electra-base-finetuned-squadv1")

Electra base ⚡ + SQuAD v1 ❓

Electra-base-discriminator fine-tuned on SQUAD v1.1 dataset for Q&A downstream task.

Details of the downstream task (Q&A) - Model 🧠

ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a GAN. At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the SQuAD 2.0 dataset.

Details of the downstream task (Q&A) - Dataset 📚

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD v1.1 contains 100,000+ question-answer pairs on 500+ articles.

Model training 🏋️‍

The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:

python transformers/examples/question-answering/run_squad.py \
  --model_type electra \
  --model_name_or_path 'google/electra-base-discriminator' \
  --do_eval \
  --do_train \
  --do_lower_case \
  --train_file '/content/dataset/train-v1.1.json' \
  --predict_file '/content/dataset/dev-v1.1.json' \
  --per_gpu_train_batch_size 16 \
  --learning_rate 3e-5 \
  --num_train_epochs 10 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir '/content/output' \
  --overwrite_output_dir \
  --save_steps 1000

Test set Results 🧾

Metric # Value
EM 83.03
F1 90.77
Size + 400 MB

Very good metrics for such a "small" model!

{
'exact': 83.03689687795648,
'f1': 90.77486052446231,
'total': 10570,
'HasAns_exact': 83.03689687795648,
'HasAns_f1': 90.77486052446231,
'HasAns_total': 10570,
'best_exact': 83.03689687795648,
'best_exact_thresh': 0.0,
'best_f1': 90.77486052446231,
'best_f1_thresh': 0.0
}

Model in action 🚀

Fast usage with pipelines:

from transformers import pipeline

QnA_pipeline = pipeline('question-answering', model='mrm8488/electra-base-finetuned-squadv1')

QnA_pipeline({
    'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.',
    'question': 'What has been discovered by scientists from China ?'
})
# Output:
{'answer': 'A new strain of flu', 'end': 19, 'score': 0.9995211430099182, 'start': 0}

Created by Manuel Romero/@mrm8488 | LinkedIn Made with in Spain