metadata
language: en
thumbnail: null
license: mit
tags:
- question-answering
- mobilebert
datasets:
- squad
metrics:
- squad
widget:
- text: Which name is also used to describe the Amazon rainforest in English?
context: >-
The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia;
Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt
amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia
or the Amazon Jungle, is a moist broadleaf forest that covers most of the
Amazon basin of South America. This basin encompasses 7,000,000 square
kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres
(2,100,000 sq mi) are covered by the rainforest. This region includes
territory belonging to nine nations. The majority of the forest is
contained within Brazil, with 60% of the rainforest, followed by Peru with
13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador,
Bolivia, Guyana, Suriname and French Guiana. States or departments in four
nations contain "Amazonas" in their names. The Amazon represents over half
of the planet's remaining rainforests, and comprises the largest and most
biodiverse tract of tropical rainforest in the world, with an estimated
390 billion individual trees divided into 16,000 species.
- text: How many square kilometers of rainforest is covered in the basin?
context: >-
The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia;
Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt
amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia
or the Amazon Jungle, is a moist broadleaf forest that covers most of the
Amazon basin of South America. This basin encompasses 7,000,000 square
kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres
(2,100,000 sq mi) are covered by the rainforest. This region includes
territory belonging to nine nations. The majority of the forest is
contained within Brazil, with 60% of the rainforest, followed by Peru with
13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador,
Bolivia, Guyana, Suriname and French Guiana. States or departments in four
nations contain "Amazonas" in their names. The Amazon represents over half
of the planet's remaining rainforests, and comprises the largest and most
biodiverse tract of tropical rainforest in the world, with an estimated
390 billion individual trees divided into 16,000 species.
MobileBERT fine-tuned on SQuAD v1
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.
This model was fine-tuned from the HuggingFace checkpoint google/mobilebert-uncased
on SQuAD1.1.
Details
Dataset | Split | # samples |
---|---|---|
SQuAD1.1 | train | 90.6K |
SQuAD1.1 | eval | 11.1k |
Fine-tuning
Python:
3.7.5
Machine specs:
CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz
Memory: 32 GiB
GPUs: 2 GeForce GTX 1070, each with 8GiB memory
GPU driver: 418.87.01, CUDA: 10.1
script:
# after install https://github.com/huggingface/transformers cd examples/question-answering mkdir -p data wget -O data/train-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json wget -O data/dev-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json export SQUAD_DIR=`pwd`/data python run_squad.py \ --model_type mobilebert \ --model_name_or_path google/mobilebert-uncased \ --do_train \ --do_eval \ --do_lower_case \ --train_file $SQUAD_DIR/train-v1.1.json \ --predict_file $SQUAD_DIR/dev-v1.1.json \ --per_gpu_train_batch_size 16 \ --per_gpu_eval_batch_size 16 \ --learning_rate 4e-5 \ --num_train_epochs 5.0 \ --max_seq_length 320 \ --doc_stride 128 \ --warmup_steps 1400 \ --output_dir $SQUAD_DIR/mobilebert-uncased-warmup-squad_v1 2>&1 | tee train-mobilebert-warmup-squad_v1.log
It took about 3 hours to finish.
Results
Model size: 95M
Metric | # Value | # Original (Table 5) |
---|---|---|
EM | 82.6 | 82.9 |
F1 | 90.0 | 90.0 |
Note that the above results didn't involve any hyperparameter search.
Example Usage
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="csarron/mobilebert-uncased-squad-v1",
tokenizer="csarron/mobilebert-uncased-squad-v1"
)
predictions = qa_pipeline({
'context': "The game was played on February 7, 2016 at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.",
'question': "What day was the game played on?"
})
print(predictions)
# output:
# {'score': 0.7754058241844177, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'}
Created by Qingqing Cao | GitHub | Twitter
Made with ❤️ in New York.