
# distilroberta-base-squad_v2

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) 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/distilroberta-base-squad_v2")
>>> tokenizer = AutoTokenizer.from_pretrained("squirro/distilroberta-base-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': 0.9498472809791565, '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: 64
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- num_devices: 8
- total_train_batch_size: 512
- 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

| Metric                   |        Value |
|:-------------------------|-------------:|
| epoch                    |      3       |
| eval_HasAns_exact        |     67.5776  |
| eval_HasAns_f1           |     74.3594  |
| eval_HasAns_total        |   5928       |
| eval_NoAns_exact         |     62.91    |
| eval_NoAns_f1            |     62.91    |
| eval_NoAns_total         |   5945       |
| eval_best_exact          |     65.2489  |
| eval_best_exact_thresh   |      0       |
| eval_best_f1             |     68.6349  |
| eval_best_f1_thresh      |      0       |
| eval_exact               |     65.2405  |
| eval_f1                  |     68.6265  |
| eval_samples             |  12165       |
| eval_total               |  11873       |
| train_loss               |      1.40336 |
| train_runtime            |   1365.28    |
| train_samples            | 131823       |
| train_samples_per_second |    289.662   |
| train_steps_per_second   |      0.567   |

### Framework versions

- Transformers 4.17.0.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 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
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- Twitter: https://twitter.com/Squirro 
- Facebook: https://www.facebook.com/squirro 
- Instagram: https://www.instagram.com/squirro/

{"language": "en", "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilroberta-base-squad_v2", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "The Stanford Question Answering Dataset", "type": "squad_v2", "args": "en"}, "metrics": [{"type": "eval_exact", "value": 65.2405}, {"type": "eval_f1", "value": 68.6265}, {"type": "eval_HasAns_exact", "value": 67.5776}, {"type": "eval_HasAns_f1", "value": 74.3594}, {"type": "eval_NoAns_exact", "value": 62.91}, {"type": "eval_NoAns_f1", "value": 62.91}]}]}]}
transformers pytorch tf onnx roberta question-answering generated_from_trainer en dataset:squad_v2 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #onnx #roberta #question-answering #generated_from_trainer #en #dataset-squad_v2 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

distilroberta-base-squad\_v2
============================


This model is a fine-tuned version of distilroberta-base 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:**


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: 64
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: tpu
* num\_devices: 8
* total\_train\_batch\_size: 512
* 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



### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.6




---


About Us
========


<img src="URL 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 Zürich, London, New York, and Singapore. Further information about AI-driven business insights can be found at URL.


Social media profiles:
----------------------


* Redefining AI Podcast (Spotify): URL
* Redefining AI Podcast (Apple Podcasts): URL
* Squirro LinkedIn: URL
* Squirro Academy LinkedIn: URL
* Twitter: URL
* Facebook: URL
* Instagram: URL



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: tpu
* num\_devices: 8
* total\_train\_batch\_size: 512
* 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 ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.6




---


About Us
========


<img src="URL 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 Zürich, London, New York, and Singapore. Further information about AI-driven business insights can be found at URL.


Social media profiles:
----------------------


* Redefining AI Podcast (Spotify): URL
* Redefining AI Podcast (Apple Podcasts): URL
* Squirro LinkedIn: URL
* Squirro Academy LinkedIn: URL
* Twitter: URL
* Facebook: URL
* Instagram: URL
TAGS
#transformers #pytorch #tf #onnx #roberta #question-answering #generated_from_trainer #en #dataset-squad_v2 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: tpu
* num\_devices: 8
* total\_train\_batch\_size: 512
* 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 ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.6




---


About Us
========


<img src="URL 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 Zürich, London, New York, and Singapore. Further information about AI-driven business insights can be found at URL.


Social media profiles:
----------------------


* Redefining AI Podcast (Spotify): URL
* Redefining AI Podcast (Apple Podcasts): URL
* Squirro LinkedIn: URL
* Squirro Academy LinkedIn: URL
* Twitter: URL
* Facebook: URL
* Instagram: URL
passage: TAGS
#transformers #pytorch #tf #onnx #roberta #question-answering #generated_from_trainer #en #dataset-squad_v2 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: tpu
* num\_devices: 8
* total\_train\_batch\_size: 512
* 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### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.6




---


About Us
========


<img src="URL 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 Zürich, London, New York, and Singapore. Further information about AI-driven business insights can be found at URL.


Social media profiles:
----------------------


* Redefining AI Podcast (Spotify): URL
* Redefining AI Podcast (Apple Podcasts): URL
* Squirro LinkedIn: URL
* Squirro Academy LinkedIn: URL
* Twitter: URL
* Facebook: URL
* Instagram: URL