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---
license: apache-2.0
language: en
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilroberta-base-squad_v2
  results:
  - task:
      name: Question Answering
      type: question-answering
    dataset:
      type: squad_v2  # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
      name: The Stanford Question Answering Dataset
      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
---

# 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 Z眉rich, London, New York, and Singapore. Further information about AI-driven business insights can be found at http://squirro.com.

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