--- language: en datasets: - squad_v2 license: cc-by-4.0 --- # roberta-base distilled into tinyroberta ## Overview **Language model:** roberta-base **Language:** English **Training data:** The PILE **Infrastructure**: 4x Tesla v100 ## Hyperparameters ``` batch_size = 96 n_epochs = 4 max_seq_len = 384 learning_rate = 1e-4 lr_schedule = LinearWarmup warmup_proportion = 0.2 teacher = "deepset/roberta-base" ``` ## Distillation 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). 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). 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. ## About us
For more info on Haystack, visit our GitHub repo and Documentation. We also have a Discord community open to everyone!
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