nvidia
/

slu_conformer_transformer_large_slurp

spoken-language-understanding

speech-intent-classification

speech-slot-filling

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steveheh commited on Oct 7, 2022

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@@ -58,6 +58,7 @@ The NeMo toolkit [4] was used for training the models for around 100 epochs. The
 The tokenizers for these models were built using the semantics annotations of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py). We use a vocabulary size of 58, including the BOS, EOS and padding tokens.
 ### Datasets

 The tokenizers for these models were built using the semantics annotations of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py). We use a vocabulary size of 58, including the BOS, EOS and padding tokens.
+Details on how to train the model can be found [here](https://github.com/NVIDIA/NeMo/blob/main/examples/slu/speech_intent_slot/README.md).
 ### Datasets