121 lines
4.0 KiB
Markdown
121 lines
4.0 KiB
Markdown
---
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datasets:
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- squad_v2
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license: cc-by-4.0
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---
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# MiniLM-L12-H384-uncased for QA
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## Overview
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**Language model:** microsoft/MiniLM-L12-H384-uncased
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**Language:** English
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**Downstream-task:** Extractive QA
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**Training data:** SQuAD 2.0
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**Eval data:** SQuAD 2.0
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**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)
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**Infrastructure**: 1x Tesla v100
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## Hyperparameters
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```
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seed=42
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batch_size = 12
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n_epochs = 4
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base_LM_model = "microsoft/MiniLM-L12-H384-uncased"
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max_seq_len = 384
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learning_rate = 4e-5
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lr_schedule = LinearWarmup
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warmup_proportion = 0.2
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doc_stride=128
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max_query_length=64
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grad_acc_steps=4
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```
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## Performance
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Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
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```
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"exact": 76.13071675229513,
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"f1": 79.49786500219953,
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"total": 11873,
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"HasAns_exact": 78.35695006747639,
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"HasAns_f1": 85.10090269418276,
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"HasAns_total": 5928,
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"NoAns_exact": 73.91084945332211,
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"NoAns_f1": 73.91084945332211,
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"NoAns_total": 5945
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```
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## Usage
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### In Transformers
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```python
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
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model_name = "deepset/minilm-uncased-squad2"
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# a) Get predictions
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nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
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QA_input = {
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'question': 'Why is model conversion important?',
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'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
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}
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res = nlp(QA_input)
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# b) Load model & tokenizer
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model = AutoModelForQuestionAnswering.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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```
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### In FARM
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```python
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from farm.modeling.adaptive_model import AdaptiveModel
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from farm.modeling.tokenization import Tokenizer
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from farm.infer import Inferencer
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model_name = "deepset/minilm-uncased-squad2"
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# a) Get predictions
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nlp = Inferencer.load(model_name, task_type="question_answering")
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QA_input = [{"questions": ["Why is model conversion important?"],
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"text": "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."}]
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res = nlp.inference_from_dicts(dicts=QA_input)
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# b) Load model & tokenizer
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model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering")
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tokenizer = Tokenizer.load(model_name)
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```
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### In haystack
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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/):
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```python
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reader = FARMReader(model_name_or_path="deepset/minilm-uncased-squad2")
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# or
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reader = TransformersReader(model="deepset/minilm-uncased-squad2",tokenizer="deepset/minilm-uncased-squad2")
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```
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## Authors
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Vaishali Pal `vaishali.pal [at] deepset.ai`
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Branden Chan: `branden.chan [at] deepset.ai`
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Timo Möller: `timo.moeller [at] deepset.ai`
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Malte Pietsch: `malte.pietsch [at] deepset.ai`
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Tanay Soni: `tanay.soni [at] deepset.ai`
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## About us
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We bring NLP to the industry via open source!
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Our focus: Industry specific language models & large scale QA systems.
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Some of our work:
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- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
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- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
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- [FARM](https://github.com/deepset-ai/FARM)
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- [Haystack](https://github.com/deepset-ai/haystack/)
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Get in touch:
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[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)
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By the way: [we're hiring!](https://apply.workable.com/deepset/)
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