Sentence-transformers

New Functions
- Multi-process tokenization (Linux only) for the model encode function. Significant speed-up when encoding large sets
- Tokenization of datasets for training can now run in parallel (Linux Only)
- New example for Quora Duplicate Questions Retrieval: See examples-folder
- Many small improvements for training better models for Information Retrieval
- Fixed LabelSampler (can be used to get batches with certain number of matching labels. Used for BatchHardTripletLoss). Moved it to DatasetFolder
- Added new Evaluators for ParaphraseMining and InformationRetrieval
- evaluation.BinaryEmbeddingSimilarityEvaluator no longer assumes a 50-50 split of the dataset. It computes the optimal threshold and measure accuracy
- model.encode - When the convert_to_numpy parameter is set, the method returns a numpy matrix instead of a list of numpy vectors
- New function: util.paraphrase_mining to perform paraphrase mining in a corpus. For an example see examples/training_quora_duplicate_questions/
- New function: util.information_retrieval to perform information retrieval / semantic search in a corpus. For an example see examples/training_quora_duplicate_questions/

Breaking Changes
- The evaluators (like EmbeddingSimilarityEvaluator) no longer accept a DataLoader as argument. Instead, the sentence and scores are directly passed. Old code that uses the previous evaluators needs to be changed. They can use the class method from_input_examples(). See examples/training_transformers/training_nli.py how to use the new evaluators.

This is a minor release. There should be no breaking changes.

- **ParallelSentencesDataset**: Datasets are tokenized on-the-fly, saving some start-up time
- **util.pytorch_cos_sim** - Method. New method to compute cosine similarity with pytorch. About 100 times faster than scipy cdist. semantic_search.py example has been updated accordingly.
- **SentenceTransformer.encode**: New parameter: *convert_to_tensor*. If set to true, encode returns one large pytorch tensor with your embeddings

This is a minor update that changes some classes for training & evaluating multilingual sentence embedding methods.

The examples for training multi-lingual sentence embeddings models have been significantly extended. See [docs/training/multilingual-models.md](https://github.com/UKPLab/sentence-transformers/blob/master/docs/training/multilingual-models.md) for details. An automatic script that downloads suitable data and extends sentence embeddings to multiple languages has been added.

The following classes/files have been changed:
- datasets/ParallelSentencesDataset.py: The dataset with parallel sentences is encoded on-the-fly, reducing the start-up time for extending a sentence embedding model to new languages. An embedding cache can be configure to store previously computed sentence embeddings during training.

New evaluation files:
- evaluation/MSEEvaluator.py - **breaking change**. Now, this class expects lists of strings with parallel (translated) sentences. The old class has been renamed to MSEEvaluatorFromDataLoader.py
- evaluation/EmbeddingSimilarityEvaluatorFromList.py - Semantic Textual Similarity data can be passed as lists of strings & scores
- evaluation/MSEEvaluatorFromDataFrame.py - MSE Evaluation of teacher and student embeddings based on data in a data frame
- evaluation/MSEEvaluatorFromDataLoader.py - MSE Evaluation if data is passed as a data loader


**Bugfixes:**
- model.encode() failed to sort sentences by length. This function has been fixed to boost encoding speed by reducing overhead of padding tokens.

This release updates HuggingFace transformers to v3.0.2. Transformers did some breaking changes to the tokenization API. This (and future) versions will not be compatible with HuggingFace transfomers v2.

There are no known breaking changes for existent models or existent code. Models trained with version 2 can be loaded without issues.

New Loss Functions
Thanks to PR 299 and 176 several new loss functions: Different triplet loss functions and ContrastiveLoss

The release update huggingface/transformers to the release v2.8.0.

New Features
- **models.Transformer**: The Transformer-Model can now load any huggingface transformers model, like BERT, RoBERTa, XLNet, XLM-R, Elextra... It is based on the AutoModel from HuggingFace. You now longer need the architecture specific models (like models.BERT, models.RoBERTa) any more. It also works with the community models.
- **Multilingual Training**: Code is released for making mono-lingual sentence embeddings models mutli-lingual. See training_multilingual.py for an example. More documentation and details will follow soon.
- **WKPooling**: Adding a pytorch implementation of SBERT-WK. Note, due to an inefficient implementation in pytorch of QR decomposition, WKPooling can only be run on the CPU, which makes it about 40 slower than mean pooling. For some models WKPooling improves the performance, for other don't.
- **WeightedLayerPooling**: A new pooling layer that uses representations from all transformer layers and learns a weighted sum of them. So far no improvement compared to only averaging the last layer.
- **New pre-trained models** released. Every available model is document in a google Spreadsheet for an easier overview.

Minor changes
- Clean-up of the examples folder.
- Model and tokenizer arguments can now be passed to the according transformers models.
- Previous version had some issues with RoBERTa and XLM-RoBERTa, that the wrong special characters were added. Everything is fixed now and relies on huggingface transformers for the correct addition of special characters to the input sentences.

Breaking changes
- *STSDataReader*: The default parameter values have been changed, so that it expects the sentences in the first two columns and the score in the third column. If you want to load the STS benchmkark dataset, you can use the STSBenchmarkDataReader.