CatalogModels
Models
The NGC catalog offers 100s of pre-trained models for computer vision, speech, recommendation, and more. Bring AI faster to market by using these models as-is or quickly build proprietary models with a fraction of your custom data.
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Conformer-Transducer-Large model for Esperanto Automatic Speech Recognition, finetuning from English SSL model on Mozilla Common Voice Esperanto 11.0 dataset.
Fourier neural operator (FNO) model. Fourier Neural Operator (FNO) is a family of resolution invariant network architectures that use spectral convolutions to learn mappings between function spaces.
For each word in the input text, the model: 1) predicts a punctuation mark that should follow the word (if any), the model supports commas, periods and question marks) and 2) predicts if the word should be capitalized or not.
Intent and Slot classification of the queries for the misty bot with DistilBert model trained on weather, smalltalk and POI (places of interest) data.
PearlNet Lang ID model for Spoken Language Identification
Base Mandarin grammar
Spanish EMEA (es-ES) Citrinet-1024 ASR model trained on ASR set 1.0
Italian (it-IT) Conformer ASR model trained on ASR set 1.0
Japanese (ja-JP) Conformer ASR model trained on ASR set 3.0
For each word in the input text, the model: predicts a punctuation mark that should follow the word (if any).
For each word in the input text, the model: predicts a punctuation mark that should follow the word (if any).
For each word in the input text, the model: 1) predicts a punctuation mark that should follow the word (if any), the model supports commas, periods and question marks) and 2) predicts if the word should be capitalized or not.
For each word in the input text, the model: 1) predicts a punctuation mark that should follow the word (if any), the model supports commas, periods, hyphens and question marks) and 2) predicts if the word should be capitalized or not.
For each word in the input text, the model: 1) predicts a punctuation mark that should follow the word (if any), the model supports commas, periods and question marks) and 2) predicts if the word should be capitalized or not.
For each word in the input text, the model predicts a punctuation mark that should follow the word (if any), the model supports commas, poornvirams, exclaimation marks and question marks.
Wide&Deep Base TensorFlow2 checkpoint trained with AMP on NVTabular preprocessed dataset with multihot embeddings
BERT Large PaddlePaddle checkpoint pretrained with LAMB optimizer using AMP
BERT Distilled 4L-288D PyTorch checkpoint distilled on SQuAD v1.1 dataset using AMP
BERT-Base PyTorch checkpoint finetuned on GLUE/SST-2 using seqLen=128
BioBERT Base Uncased Fine tuned checkpoint for Named Entity Recognition on BC5CDR Chemical dataset.
BioBERT Base Uncased Fine tuned checkpoint for Relation Extraction on ChemProt Dataset.
EfficientDet-D0 Pytorch checkpoint trained on COCO using batchsize=1200
EfficientDet-D0 TensorFlow2 checkpoint trained on COCO using batchsize=1600
efficientnet-b0 ImageNet pretrained weights