Difference between revisions of "Eosc/pretraining"
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= ELMo = | = ELMo = | ||
| + | Embeddings from Language Models (ELMo) use bidirectional LSTM language models to produce contextualized word token representations ([https://www.aclweb.org/anthology/N18-1202/ Peters et al 2018]) | ||
| + | |||
| + | == Available implementations == | ||
| + | - [https://github.com/allenai/bilm-tf Reference Tensorflow implementation]. Requirements: Python >=3.5, 1.2 < TensorFlow < 1.13 (later versions produce too many deprecation warnings), h5py. | ||
= BERT = | = BERT = | ||
Revision as of 17:10, 31 August 2020
Background
This page provides an informal, technically-oriented survey over available (and commonly used) architectures and implementations for large-scale pre-training (and fine-tuning) of contextualized neural language models.
The NLPL use case, will install, validate, and maintain a selection of these implementations, in an automated and uniform manner, on multiple HPC systems.
ELMo
Embeddings from Language Models (ELMo) use bidirectional LSTM language models to produce contextualized word token representations (Peters et al 2018)
Available implementations
- Reference Tensorflow implementation. Requirements: Python >=3.5, 1.2 < TensorFlow < 1.13 (later versions produce too many deprecation warnings), h5py.
