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- Sentiment analysis: is a text positive or negative?
- Text generation (in English): provide a prompt and the model will generate what follows.
- Name entity recognition (NER): in an input sentence, label each word with the entity it represents (person, place, etc.)
- Question answering: provide the model with some context and a question, extract the answer from the context.
- Filling masked text: given a text with masked words (e.g., replaced by [MASK]), fill the blanks.
- Summarization: generate a summary of a long text.
- Translation: translate a text in another language.
- Feature extraction: return a tensor representation of the text.
- Data Parallel (accelerator=’dp’) (multiple-gpus, 1 machine)
- DistributedDataParallel (accelerator=’ddp’) (multiple-gpus across many machines (python script based)).
- DistributedDataParallel (accelerator=’ddp_spawn’) (multiple-gpus across many machines (spawn based)).
- DistributedDataParallel 2 (accelerator=’ddp2’) (DP in a machine, DDP across machines).
- Horovod (accelerator=’horovod’) (multi-machine, multi-gpu, configured at runtime)
- TPUs (tpu_cores=8|x) (tpu or TPU pod)