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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': 0.9027367830276489, '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: 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
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