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apache-2.0 |
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This model is a fine-tuned version of albert-base-v2 on the squad_v2 dataset.
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
.
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 was done on SQuAD2.0.
The following hyperparameters were used during training:
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 |
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.
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