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+# Experiments with Sentence Transformers
+
+## GPL
+
+*Generative Pseudo-Labeling*
+
+GPL (Generative Pseudo Labeling) is an unsupervised domain adaptation method for training dense retrieval.
+
+GPL works in three steps:
+
+- `Query generation`: For each passage in the unlabeled target corpus, a query is generated using a query generation model. In [GPL paper](https://arxiv.org/abs/2112.07577), they use T5-encoder-decoder model for generate three queries for each passage.
+- `Negative mining`: For each generated queries, using dense retrival method 50 negative passages are mined from the target corpus.
+- `Pseudo labeling`: For each (query, positive passage, negative passage) tuple we compute the margin δ = CE(Q, P +) − CE(Q, P −) with CE the score as predicted by a cross-encoder, Q is query and P+ positive passage and P- negative passage.
+
+[code](./src/gpl_sample.py)
+
+## TSDAE
+
+*Transformers and Sequential Denoising Auto-Encoder*
+
+[TSDAE (Transformers and Sequential Denoising Auto-Encoder)](https://arxiv.org/abs/2104.06979) is one of most popular unsupervised training method.
+The main idea is reconstruct the original sentence from a corrupted sentence.
+
+[code](./src/tsdae.py)
+
+## References
+
+- [GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval](https://arxiv.org/abs/2112.07577)
+- [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979)