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| title: You Might Speak | |
| emoji: 🌍 | |
| colorFrom: pink | |
| colorTo: gray | |
| sdk: gradio | |
| sdk_version: 5.9.1 | |
| app_file: app.py | |
| pinned: false | |
| short_description: I guess you might speak <Language> | |
| # Language Guesser based on Name | |
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| ## Data Source | |
| https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html | |
| Last Accessed: 30th Dec 2024 | |
| ## Code Information | |
| The code is partially inspired by https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html | |
| **Changes I Introduced** | |
| - NamesDataset is separated from transformation, useful for transformation during inference | |
| - target is made integer instead of one-hot encoding; | |
| - changed the loss from combination of LogSoftmax + NLLoss to CrossEntropy (EXACTLY THE SAME STUFF); which further required removing the softmax layer from the architecture. | |
| - DataLoader is added | |
| - Input made batch first > Corresponding RNN is also made batch first. | |
| ## Evaluation | |
| Although the code is mostly replicated. However, I changed the dataloader to use apply lowercase transformation to data, and it confused the model. | |
| > Notice that the *diagonal* brightness for the **without lowercase**, which can be said that the actual class being *arabic* (for example) is guess as *arabic*. This is not the case with **with lowercase**. | |
| - Confusion matrix with **with lowercase** transformation | |
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| - Confusion matrix **without lowercase** transformation | |
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