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# Installation
We recommend **Python 3.8** or higher, **[PyTorch 1.11.0](https://pytorch.org/get-started/locally/)** or higher and **[transformers v4.32.0](https://github.com/huggingface/transformers)** or higher.
## Install SentenceTransformers
**Install with pip**
Install the *sentence-transformers* with `pip`:
```
pip install -U sentence-transformers
```
**Install with conda**
Apple silicon Installation of *sentence-transformers*
```
conda install -c conda-forge sentence-transformers
```
**Install from source**
Alternatively, you can also clone the latest version from the [repository](https://github.com/UKPLab/sentence-transformers) and install it directly from the source code:
````
pip install -e .
````
## Install PyTorch with CUDA support
If you want to use a GPU / CUDA, you must install PyTorch with the matching CUDA Version. Follow
[PyTorch - Get Started](https://pytorch.org/get-started/locally/) for further details how to install PyTorch.
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# SentenceTransformer
This page documents the properties and methods when you load a SentenceTransformer model:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("model-name")
```
```eval_rst
.. autoclass:: sentence_transformers.SentenceTransformer
:members:
:exclude-members: save_to_hub
```
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# cross_encoder
For an introduction to Cross-Encoders, see [Cross-Encoders](../../examples/applications/cross-encoder/README.md).
```eval_rst
.. autoclass:: sentence_transformers.cross_encoder.CrossEncoder
:members:
```
## Evaluation
CrossEncoder have their own evaluation classes, that are in `sentence_transformers.cross_encoder.evaluation`.
```eval_rst
.. autoclass:: sentence_transformers.cross_encoder.evaluation.CEBinaryAccuracyEvaluator
.. autoclass:: sentence_transformers.cross_encoder.evaluation.CEBinaryClassificationEvaluator
.. autoclass:: sentence_transformers.cross_encoder.evaluation.CECorrelationEvaluator
.. autoclass:: sentence_transformers.cross_encoder.evaluation.CEF1Evaluator
.. autoclass:: sentence_transformers.cross_encoder.evaluation.CESoftmaxAccuracyEvaluator
.. autoclass:: sentence_transformers.cross_encoder.evaluation.CERerankingEvaluator
```
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# Datasets
`sentence_transformers.datasets` contains classes to organize your training input examples.
## ParallelSentencesDataset
`ParallelSentencesDataset` is used for multilingual training. For details, see [multilingual training](../../examples/training/multilingual/README.md).
```eval_rst
.. autoclass:: sentence_transformers.datasets.ParallelSentencesDataset
```
## SentenceLabelDataset
`SentenceLabelDataset` can be used if you have labeled sentences and want to train with triplet loss.
```eval_rst
.. autoclass:: sentence_transformers.datasets.SentenceLabelDataset
```
## DenoisingAutoEncoderDataset
`DenoisingAutoEncoderDataset` is used for unsupervised training with the TSDAE method.
```eval_rst
.. autoclass:: sentence_transformers.datasets.DenoisingAutoEncoderDataset
```
## NoDuplicatesDataLoader
`NoDuplicatesDataLoader`can be used together with MultipleNegativeRankingLoss to ensure that no duplicates are within the same batch.
```eval_rst
.. autoclass:: sentence_transformers.datasets.NoDuplicatesDataLoader
```
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# Evaluation
`sentence_transformers.evaluation` defines different classes, that can be used to evaluate the model during training.
```eval_rst
.. autoclass:: sentence_transformers.evaluation.BinaryClassificationEvaluator
.. autoclass:: sentence_transformers.evaluation.EmbeddingSimilarityEvaluator
.. autoclass:: sentence_transformers.evaluation.InformationRetrievalEvaluator
.. autoclass:: sentence_transformers.evaluation.LabelAccuracyEvaluator
.. autoclass:: sentence_transformers.evaluation.MSEEvaluator
.. autoclass:: sentence_transformers.evaluation.MSEEvaluatorFromDataFrame
.. autoclass:: sentence_transformers.evaluation.ParaphraseMiningEvaluator
.. autoclass:: sentence_transformers.evaluation.RerankingEvaluator
.. autoclass:: sentence_transformers.evaluation.SentenceEvaluator
.. autoclass:: sentence_transformers.evaluation.SequentialEvaluator
.. autoclass:: sentence_transformers.evaluation.TranslationEvaluator
.. autoclass:: sentence_transformers.evaluation.TripletEvaluator
```
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# Losses
`sentence_transformers.losses` defines different loss functions that can be used to fine-tune embedding models on training data. The choice of loss function plays a critical role when fine-tuning the model. It determines how well our embedding model will work for the specific downstream task.
Sadly, there is no "one size fits all" loss function. Which loss function is suitable depends on the available training data and on the target task. Consider checking out the [Loss Overview](../training/loss_overview.html) to help narrow down your choice of loss function(s).
## BatchAllTripletLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.BatchAllTripletLoss
```
## BatchHardSoftMarginTripletLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.BatchHardSoftMarginTripletLoss
```
## BatchHardTripletLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.BatchHardTripletLoss
```
## BatchSemiHardTripletLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.BatchSemiHardTripletLoss
```
## ContrastiveLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.ContrastiveLoss
```
## OnlineContrastiveLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.OnlineContrastiveLoss
```
## ContrastiveTensionLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.ContrastiveTensionLoss
```
## ContrastiveTensionLossInBatchNegatives
```eval_rst
.. autoclass:: sentence_transformers.losses.ContrastiveTensionLossInBatchNegatives
```
## CoSENTLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.CoSENTLoss
```
## AnglELoss
```eval_rst
.. autoclass:: sentence_transformers.losses.AnglELoss
```
## CosineSimilarityLoss
![SBERT Siamese Network Architecture](../img/SBERT_Siamese_Network.png "SBERT Siamese Architecture")
For each sentence pair, we pass sentence A and sentence B through our network which yields the embeddings *u* und *v*. The similarity of these embeddings is computed using cosine similarity and the result is compared to the gold similarity score.
This allows our network to be fine-tuned to recognize the similarity of sentences.
```eval_rst
.. autoclass:: sentence_transformers.losses.CosineSimilarityLoss
```
## DenoisingAutoEncoderLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.DenoisingAutoEncoderLoss
```
## GISTEmbedLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.GISTEmbedLoss
```
## MSELoss
```eval_rst
.. autoclass:: sentence_transformers.losses.MSELoss
```
## MarginMSELoss
```eval_rst
.. autoclass:: sentence_transformers.losses.MarginMSELoss
```
## MatryoshkaLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.MatryoshkaLoss
```
## Matryoshka2dLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.Matryoshka2dLoss
```
## AdaptiveLayerLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.AdaptiveLayerLoss
```
## MegaBatchMarginLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.MegaBatchMarginLoss
```
## MultipleNegativesRankingLoss
*MultipleNegativesRankingLoss* is a great loss function if you only have positive pairs, for example, only pairs of similar texts like pairs of paraphrases, pairs of duplicate questions, pairs of (query, response), or pairs of (source_language, target_language).
```eval_rst
.. autoclass:: sentence_transformers.losses.MultipleNegativesRankingLoss
```
## CachedMultipleNegativesRankingLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.CachedMultipleNegativesRankingLoss
```
## MultipleNegativesSymmetricRankingLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.MultipleNegativesSymmetricRankingLoss
```
## SoftmaxLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.SoftmaxLoss
```
## TripletLoss
```eval_rst
.. autoclass:: sentence_transformers.losses.TripletLoss
```
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# Models
`sentence_transformers.models` defines different building blocks, that can be used to create SentenceTransformer networks from scratch. For more details, see [Training Overview](../training/overview.md).
## Main Classes
```eval_rst
.. autoclass:: sentence_transformers.models.Transformer
.. autoclass:: sentence_transformers.models.Pooling
.. autoclass:: sentence_transformers.models.Dense
```
## Further Classes
```eval_rst
.. autoclass:: sentence_transformers.models.Asym
.. autoclass:: sentence_transformers.models.BoW
.. autoclass:: sentence_transformers.models.CNN
.. autoclass:: sentence_transformers.models.LSTM
.. autoclass:: sentence_transformers.models.Normalize
.. autoclass:: sentence_transformers.models.WeightedLayerPooling
.. autoclass:: sentence_transformers.models.WordEmbeddings
.. autoclass:: sentence_transformers.models.WordWeights
```
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# quantization
`sentence_transformers.quantization` defines different helpful functions to quantize.
```eval_rst
.. automodule:: sentence_transformers.quantization
:members: quantize_embeddings, semantic_search_faiss, semantic_search_usearch
```
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# util
`sentence_transformers.util` defines different helpful functions to work with text embeddings.
```eval_rst
.. automodule:: sentence_transformers.util
:members: cos_sim, dot_score, paraphrase_mining, semantic_search, community_detection, http_get, truncate_embeddings
```
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# MS MARCO Cross-Encoders
[MS MARCO](https://microsoft.github.io/msmarco/) is a large scale information retrieval corpus that was created based on real user search queries using Bing search engine. The provided models can be used for semantic search, i.e., given keywords / a search phrase / a question, the model will find passages that are relevant for the search query.
The training data consists of over 500k examples, while the complete corpus consists of over 8.8 million passages.
## Usage with SentenceTransformers
Pre-trained models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder("model_name", max_length=512)
scores = model.predict(
[("Query", "Paragraph1"), ("Query", "Paragraph2"), ("Query", "Paragraph3")]
)
```
## Usage with Transformers
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained("model_name")
tokenizer = AutoTokenizer.from_pretrained("model_name")
features = tokenizer(["Query", "Query"], ["Paragraph1", "Paragraph2"], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
```
## Models & Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset.
| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec |
| ------------- | :-------------: | :-----: | ---: |
| **Version 2 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000
| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
| **Version 1 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000 |
| cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900 |
| cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680 |
| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340 |
| **Other models** | | | |
| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900 |
| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340 |
| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100 |
| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340 |
| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330 |
| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
Note: Runtime was computed on a V100 GPU with Huggingface Transformers v4.
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