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--- |
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language: en |
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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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# tinyroberta-squad2 |
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## Overview |
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**Language model:** tinyroberta-squad2 |
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**Language:** English |
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**Training data:** The PILE |
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**Code:** |
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**Infrastructure**: 4x Tesla v100 |
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## Hyperparameters |
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``` |
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batch_size = 96 |
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n_epochs = 4 |
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base_LM_model = "deepset/tinyroberta-squad2-step1" |
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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 = 0.2 |
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teacher = "deepset/roberta-base" |
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``` |
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## Distillation |
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This model was distilled using the TinyBERT approach described in [this paper](https://arxiv.org/pdf/1909.10351.pdf) and implemented in [haystack](https://github.com/deepset-ai/haystack). |
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We have performed intermediate layer distillation with roberta-base as the teacher which resulted in [deepset/tinyroberta-6l-768d](https://huggingface.co/deepset/tinyroberta-6l-768d). |
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This model has not been distilled for any specific task. If you are interested in using distillation to improve its performance on a downstream task, you can take advantage of haystack's new [distillation functionality](https://haystack.deepset.ai/guides/model-distillation). You can also check out [deepset/tinyroberta-squad2](https://huggingface.co/deepset/tinyroberta-squad2) for a model that is already distilled on an extractive QA downstream task. |
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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/tinyroberta-squad2" |
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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/tinyroberta-squad2" |
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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/roberta-base-squad2") |
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# or |
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reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2") |
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``` |
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## Authors |
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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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Michel Bartels: `michel.bartels [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!](http://www.deepset.ai/jobs) |
