Back to all models
question-answering mask_token:
Context
Query this model
🔥 This model is currently loaded and running on the Inference API. ⚠️ This model could not be loaded by the inference API. ⚠️ This model can be loaded on the Inference API on-demand.
JSON Output
API endpoint  

⚡️ Upgrade your account to access the Inference API

Share Copied link to clipboard

Monthly model downloads

mrm8488/mobilebert-uncased-finetuned-squadv1 mrm8488/mobilebert-uncased-finetuned-squadv1
47 downloads
last 30 days

pytorch

tf

Contributed by

mrm8488 Manuel Romero
156 models

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

			
Copy to clipboard
from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mrm8488/mobilebert-uncased-finetuned-squadv1") model = AutoModelForQuestionAnswering.from_pretrained("mrm8488/mobilebert-uncased-finetuned-squadv1")

MobileBERT + SQuAD (v1.1) 📱❓

mobilebert-uncased fine-tuned on SQUAD v2.0 dataset for Q&A downstream task.

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

MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks.

The checkpoint used here is the original MobileBert Optimized Uncased English: (uncased_L-24_H-128_B-512_A-4_F-4_OPT) checkpoint.

More about the model here

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 bert \
  --model_name_or_path 'google/mobilebert-uncased' \
  --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 5 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir '/content/output' \
  --overwrite_output_dir \
  --save_steps 1000

It is important to say that this models converges much faster than other ones. So, it is also cheap to fine-tune.

Test set Results 🧾

Metric # Value
EM 82.33
F1 89.64
Size 94 MB

Model in action 🚀

Fast usage with pipelines:

from transformers import pipeline
QnA_pipeline = pipeline('question-answering', model='mrm8488/mobilebert-uncased-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': 'Who did identified it ?'
    })

# Output: {'answer': 'scientists.', 'end': 106, 'score': 0.7885545492172241, 'start': 96}

Created by Manuel Romero/@mrm8488 | LinkedIn

Made with in Spain