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README.md

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+# kwang2049/TSDAE-cqadupstack2nli_stsb
+This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model adapts the knowledge from the NLI and STSb data to the specific domain cqadupstack. Training procedure of this model:
+ 1. Initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased);
+ 2. Unsupervised training on cqadupstack with the TSDAE objective;
+ 3. Supervised training on the NLI data with cross-entropy loss;
+ 4. Supervised training on the STSb data with MSE loss.
+ 
+ The pooling method is CLS-pooling.
+ 
+ ## Usage
+ To use this model, an convenient way is through [SentenceTransformers](https://github.com/UKPLab/sentence-transformers). So please install it via:
+ ```bash
+ pip install sentence-transformers
+ ```
+ And then load the model and use it to encode sentences:
+ ```python
+ from sentence_transformers import SentenceTransformer, models
+ dataset = 'cqadupstack'
+ model_name_or_path = f'kwang2049/TSDAE-{dataset}2nli_stsb'
+ model = SentenceTransformer(model_name_or_path)
+ model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
+ sentence_embeddings = model.encode(['This is the first sentence.', 'This is the second one.'])
+ ```
+ ## Evaluation
+ To evaluate the model against the datasets used in the paper, please install our evaluation toolkit [USEB](https://github.com/UKPLab/useb):
+ ```bash
+ pip install useb  # Or git clone and pip install .
+ python -m useb.downloading all  # Download both training and evaluation data
+ ```
+ And then do the evaluation:
+ ```python
+ from sentence_transformers import SentenceTransformer, models
+import torch
+from useb import run_on
+dataset = 'cqadupstack'
+model_name_or_path = f'kwang2049/TSDAE-{dataset}2nli_stsb'
+model = SentenceTransformer(model_name_or_path)
+model[1] = models.Pooling(model[0].get_word_embedding_dimension(), pooling_mode='cls')  # Note this model uses CLS-pooling
+@torch.no_grad()
+def semb_fn(sentences) -> torch.Tensor:
+    return torch.Tensor(model.encode(sentences, show_progress_bar=False))
+result = run_on(
+    dataset,
+    semb_fn=semb_fn,
+    eval_type='test',
+    data_eval_path='data-eval'
+)
+ ```
+ 
+ ## Training
+ Please refer to [the page of TSDAE training](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE) in SentenceTransformers.
+ 
+ ## Cite & Authors
+ If you use the code for evaluation, feel free to cite our publication [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979):
+```bibtex 
+@article{wang-2021-TSDAE,
+    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
+    author = "Wang, Kexin and Reimers, Nils and  Gurevych, Iryna", 
+    journal= "arXiv preprint arXiv:2104.06979",
+    month = "4",
+    year = "2021",
+    url = "https://arxiv.org/abs/2104.06979",
+}
+```