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README.md
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---
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language:
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- multilingual
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- en
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- de
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license: mit
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widget:
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- text: "ich glaub ich muss echt rewatchen like i [MASK] so empty was soll ich denn jetzt machen"
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example_title: "Example 1"
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- text: "I don't get [MASK] er damit erreichen will."
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example_title: "Example 2"
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- text: "Sagt ein(e) Head(in) [MASK] research! Researchen Sie mal ein bisschen mehr."
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example_title: "Example 3"
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---
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# German-English Code-Switching BERT
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A BERT-based model trained with masked language modelling on a large corpus of German--English code-switching. It was introduced in [this paper](). This model is case sensitive.
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## Overview
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- **Initializd language model:** bert-base-multilingual-cased
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- **Training data:** The TongueSwitcher Corpus
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- **Infrastructure**: 4x Nvidia A100 GPUs
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- **Published**: 16 October 2023
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## Hyperparameters
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```
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batch_size = 32
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n_steps = 191,950
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max_seq_len = 512
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learning_rate = 1e-4
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weight_decay = 0.01
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Adam beta = (0.9, 0.999)
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lr_schedule = LinearWarmup
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num_warmup_steps = 10,000
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seed = 2021
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```
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## Performance
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During training we monitored the evaluation loss on the TongueSwitcher dev set.
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## Authors
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- Igor Sterner: `is473 [at] cam.ac.uk`
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- Simone Teufel: `sht25 [at] cam.ac.uk`
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### BibTeX entry and citation info
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```bibtex
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@inproceedings{sterner2023tongueswitcher,
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author = {Igor Sterner and Simone Teufel},
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title = {TongueSwitcher: Fine-Grained Identification of German-English Code-Switching},
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booktitle = {Sixth Workshop on Computational Approaches to Linguistic Code-Switching},
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year = {2023},
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}
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```
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