|
--- |
|
license: apache-2.0 |
|
language: en |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- squad_v2 |
|
model-index: |
|
- name: albert-base-v2-squad_v2 |
|
results: |
|
- task: |
|
name: Question Answering |
|
type: question-answering |
|
dataset: |
|
type: squad_v2 |
|
name: The Stanford Question Answering Dataset |
|
args: en |
|
metrics: |
|
- type: eval_exact |
|
value: 78.8175 |
|
- type: eval_f1 |
|
value: 81.9984 |
|
- type: eval_HasAns_exact |
|
value: 75.3374 |
|
- type: eval_HasAns_f1 |
|
value: 81.7083 |
|
- type: eval_NoAns_exact |
|
value: 82.2876 |
|
- type: eval_NoAns_f1 |
|
value: 82.2876 |
|
--- |
|
|
|
# albert-base-v2-squad_v2 |
|
|
|
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/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](https://rajpurkar.github.io/SQuAD-explorer/). |
|
|
|
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:__ |
|
|
|
```python |
|
>>> 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](https://huggingface.co/datasets/squad_v2). |
|
|
|
|
|
## 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 |
|
|
|
<img src="https://squirro.com/wp-content/themes/squirro/img/squirro_logo.svg" alt="Squirro Logo" width="250"/> |
|
|
|
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 Z眉rich, 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/ |
