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Getting Started

  • Introduction
  • Tutorials
  • Best Practices

NeMo Core

  • NeMo Models
  • Experiment Manager
  • Neural Types
  • Exporting NeMo Models
  • Adapters
    • Adapter Components
    • Adapters API
  • Core APIs

Speech Processing

  • Automatic Speech Recognition (ASR)
    • Models
    • Datasets
    • ASR Language Modeling
    • Checkpoints
    • Scores
    • NeMo ASR Configuration Files
    • NeMo ASR collection API
    • Resources and Documentation
    • Example: Kinyarwanda ASR using Mozilla Common Voice Dataset
  • Speech Classification
    • Models
    • Datasets
    • Checkpoints
    • NeMo Speech Classification Configuration Files
    • Resource and Documentation Guide
  • Speaker Recognition (SR)
    • Models
    • NeMo Speaker Recognition Configuration Files
    • Datasets
    • Checkpoints
    • NeMo Speaker Recognition API
    • Resource and Documentation Guide
  • Speaker Diarization
    • Models
    • Datasets
    • Checkpoints
    • NeMo Speaker Diarization Configuration Files
    • NeMo Speaker Diarization API
    • Resource and Documentation Guide
  • Self-Supervised Learning
    • Models
    • Datasets
    • Checkpoints
    • NeMo SSL Configuration Files
    • NeMo SSL collection API
    • Resources and Documentation
  • Speech Intent Classification and Slot Filling
    • Models
    • Datasets
    • Checkpoints
    • NeMo Speech Intent Classification and Slot Filling Configuration Files
    • NeMo Speech Intent Classification and Slot Filling collection API
    • Resources and Documentation

Natural Language Processing

  • NeMo Megatron
    • Migrating from Megatron-LM
    • GPT model training
    • Batching
    • Parallelisms
    • Prompt Learning
    • Coming Soon …
  • Machine Translation Models
  • (Inverse) Text Normalization
    • WFST-based (Inverse) Text Normalization
      • Text (Inverse) Normalization
      • Grammar customization
      • Deploy to Production with C++ backend
      • Resources and Documentation
    • Neural Models for (Inverse) Text Normalization
      • Neural Text Normalization Models
      • Thutmose Tagger: Single-pass Tagger-based ITN Model
  • NeMo NLP collection API
  • Tasks
    • Punctuation And Capitalization Models
      • Punctuation and Capitalization Model
      • Punctuation and Capitalization Lexical Audio Model
    • Token Classification (Named Entity Recognition) Model
    • Joint Intent and Slot Classification
    • Text Classification model
    • BERT
    • Language Modeling
    • Prompt Learning
    • Question Answering
    • Dialogue tasks
    • GLUE Benchmark
    • Information Retrieval
    • Entity Linking
    • Model NLP
    • Machine Translation Models

Text To Speech (TTS)

  • Text-to-Speech (TTS)
    • Models
    • Data Preprocessing
    • Checkpoints
    • NeMo TTS Configuration Files
    • NeMo TTS Collection API
    • Resources and Documentation
    • Grapheme-to-Phoneme Models

Text Processing

  • Common Collection
    • Callbacks
    • Losses
    • Metrics
    • Tokenizers

Tools

  • Tools
    • Dataset Creation Tool Based on CTC-Segmentation
    • Speech Data Explorer
    • Comparison tool for ASR Models
    • ASR Evaluator
    • Speech Data Processor
  • .rst

GLUE Benchmark

GLUE Benchmark#

We recommend you try the GLUE Benchmark model in a Jupyter notebook (can run on Google’s Colab): NeMo/tutorials/nlp/GLUE_Benchmark.ipynb.

Connect to an instance with a GPU (Runtime -> Change runtime type -> select GPU for the hardware accelerator).

An example script on how to train the model can be found here: NeMo/examples/nlp/glue_benchmark/glue_benchmark.py.

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Dialogue tasks

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Information Retrieval

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Last updated on Mar 25, 2023.