|
--- |
|
datasets: |
|
- squad_v2 |
|
license: cc-by-4.0 |
|
--- |
|
|
|
# roberta-base for QA |
|
|
|
NOTE: This is version 2 of the model. See [this github issue](https://github.com/deepset-ai/FARM/issues/552) from the FARM repository for an explanation of why we updated. If you'd like to use version 1, specify `revision="v1.0"` when loading the model in Transformers 3.5. For exmaple: |
|
``` |
|
model_name = "deepset/roberta-base-squad2" |
|
pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question-answering") |
|
``` |
|
|
|
## Overview |
|
**Language model:** roberta-base |
|
**Language:** English |
|
**Downstream-task:** Extractive QA |
|
**Training data:** SQuAD 2.0 |
|
**Eval data:** SQuAD 2.0 |
|
**Code:** See [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) in [FARM](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) |
|
**Infrastructure**: 4x Tesla v100 |
|
|
|
## Hyperparameters |
|
|
|
``` |
|
batch_size = 96 |
|
n_epochs = 2 |
|
base_LM_model = "roberta-base" |
|
max_seq_len = 386 |
|
learning_rate = 3e-5 |
|
lr_schedule = LinearWarmup |
|
warmup_proportion = 0.2 |
|
doc_stride=128 |
|
max_query_length=64 |
|
``` |
|
|
|
## Performance |
|
Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). |
|
|
|
``` |
|
"exact": 79.97136359807968 |
|
"f1": 83.00449234495325 |
|
|
|
"total": 11873 |
|
"HasAns_exact": 78.03643724696356 |
|
"HasAns_f1": 84.11139298441825 |
|
"HasAns_total": 5928 |
|
"NoAns_exact": 81.90075693860386 |
|
"NoAns_f1": 81.90075693860386 |
|
"NoAns_total": 5945 |
|
``` |
|
|
|
## Usage |
|
|
|
### In Transformers |
|
```python |
|
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline |
|
|
|
model_name = "deepset/roberta-base-squad2" |
|
|
|
# a) Get predictions |
|
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) |
|
QA_input = { |
|
'question': 'Why is model conversion important?', |
|
'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' |
|
} |
|
res = nlp(QA_input) |
|
|
|
# b) Load model & tokenizer |
|
model = AutoModelForQuestionAnswering.from_pretrained(model_name) |
|
tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
``` |
|
|
|
### In FARM |
|
|
|
```python |
|
from farm.modeling.adaptive_model import AdaptiveModel |
|
from farm.modeling.tokenization import Tokenizer |
|
from farm.infer import Inferencer |
|
|
|
model_name = "deepset/roberta-base-squad2" |
|
|
|
# a) Get predictions |
|
nlp = Inferencer.load(model_name, task_type="question_answering") |
|
QA_input = [{"questions": ["Why is model conversion important?"], |
|
"text": "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."}] |
|
res = nlp.inference_from_dicts(dicts=QA_input, rest_api_schema=True) |
|
|
|
# b) Load model & tokenizer |
|
model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering") |
|
tokenizer = Tokenizer.load(model_name) |
|
``` |
|
|
|
### In haystack |
|
For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/): |
|
```python |
|
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2") |
|
# or |
|
reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2") |
|
``` |
|
|
|
|
|
## Authors |
|
Branden Chan: `branden.chan [at] deepset.ai` |
|
Timo M枚ller: `timo.moeller [at] deepset.ai` |
|
Malte Pietsch: `malte.pietsch [at] deepset.ai` |
|
Tanay Soni: `tanay.soni [at] deepset.ai` |
|
|
|
## About us |
|
 |
|
|
|
We bring NLP to the industry via open source! |
|
Our focus: Industry specific language models & large scale QA systems. |
|
|
|
Some of our work: |
|
- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) |
|
- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) |
|
- [FARM](https://github.com/deepset-ai/FARM) |
|
- [Haystack](https://github.com/deepset-ai/haystack/) |
|
|
|
Get in touch: |
|
[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) |
|
|
|
By the way: [we're hiring!](https://apply.workable.com/deepset/) |
|
|
