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--- |
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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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# electra-base for QA |
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## Overview |
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**Language model:** electra-base |
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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 = 32 |
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n_epochs = 5 |
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base_LM_model = "google/electra-base-discriminator" |
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max_seq_len = 384 |
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learning_rate = 1e-4 |
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lr_schedule = LinearWarmup |
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warmup_proportion = 0.1 |
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doc_stride=128 |
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max_query_length=64 |
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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": 77.30144024256717, |
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"f1": 81.35438272008543, |
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"total": 11873, |
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"HasAns_exact": 74.34210526315789, |
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"HasAns_f1": 82.45961302894314, |
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"HasAns_total": 5928, |
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"NoAns_exact": 80.25231286795626, |
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"NoAns_f1": 80.25231286795626, |
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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/electra-base-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/electra-base-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/electra-base-squad2") |
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# or |
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reader = TransformersReader(model="deepset/electra-base-squad2",tokenizer="deepset/electra-base-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 M枚ller: `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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Note: |
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Borrowed this model from Haystack model repo for adding tensorflow model. |
