Contents¶
Getting Started
Frequently Asked Questions
- How do I use my v2 models in v3 ?
- How do I train the Transformer model?
- Performance tips
- Position encoding: Absolute vs Relative vs Rotary Embeddings vs Alibi
- Do you support multi-gpu?
- How do I use Pretrained embeddings (e.g. GloVe)?
- How can I ensemble Models at inference?
- How can I weight different corpora at training?
- What special tokens does OpenNMT-py use?
- How can I apply on-the-fly tokenization and subword regularization when training?
- What are the readily available on-the-fly data transforms?
- How can I create custom on-the-fly data transforms?
- How to use LoRa and 8bit loading to finetune a big model ?
- How to use gradient checkpointing when dealing with a big model ?
- Can I get word alignments while translating?
- How can I update a checkpoint’s vocabulary?
- How can I use source word features?
- How can I set up a translation server ?
Examples
Scripts
- Build Vocab
- Configuration
- Data
- Vocab
- Features
- Transform/SwitchOut
- Transform/Token_Drop
- Transform/Token_Mask
- Transform/InferFeats
- Transform/Normalize
- Transform/Clean
- Transform/Docify
- Transform/BART
- Transform/InlineTags
- Transform/FuzzyMatching
- Transform/Subword/Common
- Transform/Subword/ONMTTOK
- Transform/Uppercase
- Transform/Terminology
- Transform/Filter
- Transform/Prefix
- Transform/Suffix
- Reproducibility
- Train
- Configuration
- Data
- Vocab
- Features
- Pruning
- Embeddings
- Transform/SwitchOut
- Transform/Token_Drop
- Transform/Token_Mask
- Transform/InferFeats
- Transform/Normalize
- Transform/Clean
- Transform/Docify
- Transform/BART
- Transform/InlineTags
- Transform/FuzzyMatching
- Transform/Subword/Common
- Transform/Subword/ONMTTOK
- Transform/Uppercase
- Transform/Terminology
- Transform/Filter
- Transform/Prefix
- Transform/Suffix
- Distributed
- Model-Embeddings
- Model-Embedding Features
- Model- Task
- Model- Encoder-Decoder
- Model- Attention
- Model - Alignement
- Generator
- General
- Reproducibility
- Initialization
- Optimization- Type
- Optimization- Rate
- Logging
- Dynamic data
- Quant options
- Translate
- Configuration
- Model
- Data
- Features
- Beam Search
- Random Sampling
- Reproducibility
- Penalties
- Decoding tricks
- Logging
- Distributed
- Efficiency
- Transform/SwitchOut
- Transform/Token_Drop
- Transform/Token_Mask
- Transform/InferFeats
- Transform/Normalize
- Transform/Clean
- Transform/Docify
- Transform/BART
- Transform/InlineTags
- Transform/FuzzyMatching
- Transform/Subword/Common
- Transform/Subword/ONMTTOK
- Transform/Uppercase
- Transform/Terminology
- Transform/Filter
- Transform/Prefix
- Transform/Suffix
- Quant options
- Server
API