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Running on Zero
Running on Zero
| ## Zero-Shot Approximation of Language Embeddings | |
| This directory contains all scripts that are needed to reproduce the meta learning for zero-shot part of our system. These scripts allow you to predict representations of languages purely based on distances between them, as measured by a variety of linguistically informed metrics, or even better, a learned combination thereof. | |
| ### Learned distance metric | |
| If you want to use a learned distance metric, you need to run `MetricMetaLearner.py` first to generate a lookup file for the learned distances. | |
| Note: **The learned distances are (obviously) only useful for the model it was trained on**, i.e., different Toucan models require different learned-distance lookups. | |
| ### Applying zero-shot approximation to a trained model | |
| Use `run_zero_shot_lang_emb_injection.py` to update the language embeddings of a trained model for all languages that were *not* seen during training (by default, `supervised_languages.json` is used to determine which languages *were* seen). | |
| See the script for arguments that can be passed (e.g. to use a custom model path). Here is an example: | |
| ``` | |
| python run_zero_shot_lang_emb_injection.py -m <model_path> -d <distance_type> -k <number_of_nearest_neighbors> | |
| ``` | |
| By default, the updated model is saved with a modified filename in the same directory. |