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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."}' \
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mrm8488/roberta-base-1B-1-finetuned-squadv1 mrm8488/roberta-base-1B-1-finetuned-squadv1
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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/roberta-base-1B-1-finetuned-squadv1") model = AutoModelForQuestionAnswering.from_pretrained("mrm8488/roberta-base-1B-1-finetuned-squadv1")

RoBERTa-base (1B-1) + SQuAD v1 ❓

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

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

RoBERTa Pretrained on Smaller Datasets

NYU Machine Learning for Language pretrained RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). They released 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: They combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.

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/ \
  --model_type roberta \
  --model_name_or_path 'nyu-mll/roberta-base-1B-1' \
  --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 72.62
F1 82.19
'exact': 72.62062440870388,
'f1': 82.19430877136834,
'total': 10570,
'HasAns_exact': 72.62062440870388,
'HasAns_f1': 82.19430877136834,
'HasAns_total': 10570,
'best_exact': 72.62062440870388,
'best_exact_thresh': 0.0,
'best_f1': 82.19430877136834,
'best_f1_thresh': 0.0

Model in action 🚀

Fast usage with pipelines:

from transformers import pipeline

QnA_pipeline = pipeline('question-answering', model='mrm8488/roberta-base-1B-1-finetuned-squadv1')

    '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': 0.04702283976040074, 'start': 0}

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