bert-in-production
github.com/domhudson/bert-in-production ↗A collection of resources on using BERT (https://arxiv.org/abs/1810.04805 ) and related Language Models in production environments.
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ImplementationsDescriptive ResourcesDeep AnalysisGeneral ResourcesSpeedOther Resources
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Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me compression resources from bert-in-production"
Installation instructions →What's inside
Speed
- ALBERT: A Lite BERT for Self-supervised Learning of Language RepresentationsCompression
- Compression BERT for faster predictionCompression
- DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighterKnowledge Distillation
- Distilling Task-Specific Knowledge from BERT into Simple Neural NetworksKnowledge Distillation
- Extreme Language Model Compression with Optimal Subwords and Shared ProjectionsCompression
- PoWER-BERT: Accelerating BERT inference for Classification TasksCompression
Deep Analysis
General Resources
Descriptive Resources
Implementations
Other Resources
- Deploying BERT in production
- Improving Neural Machine Translation with Parent-Scaled Self-Attention
- Pruning bert to accelerate inference
- Reducing Transformer Depth on Demand with Structured Dropout
- RoBERTa: A Robustly Optimized BERT Pretraining Approach
- Serving Google BERT in Production using Tensorflow and ZeroMQ
Showing a sample of 43 resources. View the full list on GitHub →