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Displaying 56 results

Kaldi
NVIDIA
Kaldi is an open-source software framework for speech processing.
Container
Text Classification with BERT and NeMo
NVIDIA
Text Classification with BERT and NeMo. This NeMo application trains text classification models using single-GPU or multi-GPU. We log performance metrics and visualize them with TensorBoard. We show how to do inference with NeMo, and we visualize BERT embeddings before and after fine-tuning.
Container
DeepPavlov
DeepPavlov
DeepPavlov is an open-source conversational AI library built on TensorFlow and Keras. DeepPavlov is designed for development of production ready chatbots and complex conversational systems, research in the area of NLP and, particularly, of dialog systems.
Container
Fine-Tune and Optimize BERT
NVIDIA
Jupyter Notebooks for BERT Pre-training, Fine-Tuning and Inference profiling and optimization via TensorFlow, AMP, XLA, DLProf, TF-TRT and Triton.
Container
Megatron GPT2 345M
NVIDIA NeMo
345M parameter GPT generative Megatron model
Model
BertLargeUncasedForNemo
NVIDIA
BERT Large Model trained with NeMo on uncased Wikipedia and Bookcorpus on a sequence length 512.
Model
BertBaseUncasedForNemo
NVIDIA
BERT Base Model trained on uncased Wikipedia and BookCorpus dataset on a sequence length of 512.
Model
BioMegatron345mCased
—
Megatron pretrained on cased biomedical dataset PubMed with 345 million parameters.
Model
BioMegatron345mUncasedSQuADv1
NVIDIA
BioMegatron 345M uncased model for Question Answering finetuned with NeMo on SQuAD v1.1 dataset.
Model
BertBaseUncasedSQuADv1
NVIDIA
BERT Base Uncased model for Question Answering finetuned with NeMo on SQuAD v1.1 dataset.
Model
BioBERTBaseCasedForNeMo
Nvidia
Model
BERT PyTorch checkpoint (Dist-4L-288D, SQuAD1, seqLen384, AMP)
NVIDIA Deep Learning Examples
BERT Distilled 4L-288D PyTorch checkpoint distilled on SQuAD v1.1 dataset using AMP
Model
BertBaseCasedForNeMo
NVIDIA
BERT Base Model trained with NeMo on cased Wikipedia and BookCorpus on a sequence length of 512.
Model
BERT PyTorch checkpoint (Large, SST-2, seqLen128, AMP)
NVIDIA Deep Learning Examples
BERT Large PyTorch checkpoint finetuned on GLUE/SST-2 dataset using AMP
Model
TRADE - MultiWOZ 2.1 - Dialogue State Tracker
NVIDIA
Checkpoint of TRADE model for dialogue state tracking trained on MultiWOZ 2.1 dataset using NeMo.
Model
TRADE - MultiWOZ 2.0 - Dialogue State Tracker
NVIDIA
Checkpoint of TRADE model for dialogue state tracking trained on MultiWOZ 2.0 dataset using NeMo.
Model
Bert Large checkpoint (TensorFlow2, AMP, Squad1.1, seqLen384)
NVIDIA Deep Learning Examples
Bert Large TensorFlow2 checkpoint finetuned on Squad1.1 using seqLen=384
Model
BERT PaddlePaddle checkpoint (Large, Pretraining, AMP, LAMB)
NVIDIA Deep Learning Examples
BERT Large PaddlePaddle checkpoint pretrained with LAMB optimizer using AMP
Model
BERT PyTorch checkpoint (Dist-6L-768D, SQuAD1, AMP)
NVIDIA Deep Learning Examples
BERT Distilled 6L-768D PyTorch checkpoint distilled on SQuAD v1.1 dataset using AMP
Model
BERT PyTorch checkpoint (Base, Pretraining, AMP, LAMB)
NVIDIA Deep Learning Examples
BERT Base PyTorch checkpoint pretrained with LAMB optimizer using AMP
Model
BERT PyTorch checkpoint (Dist-6L-768D, Pretraining, AMP)
NVIDIA Deep Learning Examples
BERT Distilled 6L-768D PyTorch Phase2 checkpoint pretrained using 67K steps on seqLen128 and 6k steps on seqLen512
Model
BERT PyTorch checkpoint (Dist-4L-288D, Pretraining, AMP)
NVIDIA Deep Learning Examples
BERT Distilled 4L-288D PyTorch checkpoint distilled on pretraining dataset using AMP
Model
BART PyT checkpoint (Summarization, XSum)
NVIDIA Deep Learning Examples
BART PyT checkpoint for summarization on XSum dataset
Model
Bert Large checkpoint (TensorFlow2, Pretraining AMP, LAMB)
NVIDIA Deep Learning Examples
Bert Large TensorFlow2 checkpoint pretrained using AMP and LAMB optimizer
Model
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