albert-base-v2-squad_v2

This model is a fine-tuned version of albert-base-v2 on the squad_v2 dataset.

Model description

This model is fine-tuned on the extractive question answering task -- The Stanford Question Answering Dataset -- SQuAD2.0.

For convenience this model is prepared to be used with the frameworks PyTorch, Tensorflow and ONNX.

Intended uses & limitations

This model can handle mismatched question-context pairs. Make sure to specify handle_impossible_answer=True when using QuestionAnsweringPipeline.

Example usage:

>>> from transformers import AutoModelForQuestionAnswering, AutoTokenizer, QuestionAnsweringPipeline
>>> model = AutoModelForQuestionAnswering.from_pretrained("squirro/albert-base-v2-squad_v2")
>>> tokenizer = AutoTokenizer.from_pretrained("squirro/albert-base-v2-squad_v2")
>>> qa_model = QuestionAnsweringPipeline(model, tokenizer)
>>> qa_model(
>>>    question="What's your name?",
>>>    context="My name is Clara and I live in Berkeley.",
>>>    handle_impossible_answer=True  # important!
>>> )
{'score': 0.9027367830276489, 'start': 11, 'end': 16, 'answer': 'Clara'}

Training and evaluation data

Training and evaluation was done on SQuAD2.0.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 8
seed: 42
distributed_type: tpu
num_devices: 8
total_train_batch_size: 256
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

key	value
epoch	3
eval_HasAns_exact	75.3374
eval_HasAns_f1	81.7083
eval_HasAns_total	5928
eval_NoAns_exact	82.2876
eval_NoAns_f1	82.2876
eval_NoAns_total	5945
eval_best_exact	78.8175
eval_best_exact_thresh	0
eval_best_f1	81.9984
eval_best_f1_thresh	0
eval_exact	78.8175
eval_f1	81.9984
eval_samples	12171
eval_total	11873
train_loss	0.775293
train_runtime	1402
train_samples	131958
train_samples_per_second	282.363
train_steps_per_second	1.104

Framework versions

Transformers 4.18.0.dev0
Pytorch 1.9.0+cu111
Datasets 1.18.3
Tokenizers 0.11.6

About Us

Squirro marries data from any source with your intent, and your context to intelligently augment decision-making - right when you need it!

An Insight Engine at its core, Squirro works with global organizations, primarily in financial services, public sector, professional services, and manufacturing, among others. Customers include Bank of England, European Central Bank (ECB), Deutsche Bundesbank, Standard Chartered, Henkel, Armacell, Candriam, and many other world-leading firms.

Founded in 2012, Squirro is currently present in Zürich, London, New York, and Singapore. Further information about AI-driven business insights can be found at http://squirro.com.

Social media profiles:

Redefining AI Podcast (Spotify): https://open.spotify.com/show/6NPLcv9EyaD2DcNT8v89Kb
Redefining AI Podcast (Apple Podcasts): https://podcasts.apple.com/us/podcast/redefining-ai/id1613934397
Squirro LinkedIn: https://www.linkedin.com/company/squirroag
Squirro Academy LinkedIn: https://www.linkedin.com/showcase/the-squirro-academy
Twitter: https://twitter.com/Squirro
Facebook: https://www.facebook.com/squirro
Instagram: https://www.instagram.com/squirro/

squirro
/

albert-base-v2-squad_v2