Pretrained Models¶
We provide various pre-trained models. Using these models is easy:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('model_name')
All models are hosted on the HuggingFace Model Hub.
Model Overview¶
The following table provides an overview of (selected) models. They have been extensively evaluated for their quality to embedded sentences (Performance Sentence Embeddings) and to embedded search queries & paragraphs (Performance Semantic Search).
The all-* models where trained on all available training data (more than 1 billion training pairs) and are designed as general purpose models. The all-mpnet-base-v2 model provides the best quality, while all-MiniLM-L6-v2 is 5 times faster and still offers good quality. Toggle All models to see all evaluated models or visit HuggingFace Model Hub to view all existing sentence-transformers models.
Semantic Search¶
The following models have been specifically trained for Semantic Search: Given a question / search query, these models are able to find relevant text passages. For more details, see Usage - Semantic Search.
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1')
query_embedding = model.encode('How big is London')
passage_embedding = model.encode(['London has 9,787,426 inhabitants at the 2011 census',
'London is known for its finacial district'])
print("Similarity:", util.dot_score(query_embedding, passage_embedding))
Multi-QA Models¶
The following models have been trained on 215M question-answer pairs from various sources and domains, including StackExchange, Yahoo Answers, Google & Bing search queries and many more. These model perform well across many search tasks and domains.
These models were tuned to be used with dot-product:
| Model | Performance Semantic Search (6 Datasets) | Queries (GPU / CPU) per sec. |
|---|---|---|
| multi-qa-MiniLM-L6-dot-v1 | 49.19 | 18,000 / 750 |
| multi-qa-distilbert-dot-v1 | 52.51 | 7,000 / 350 |
| multi-qa-mpnet-base-dot-v1 | 57.60 | 4,000 / 170 |
These models produce normalized vectors of length 1, which can be used with dot-product, cosine-similarity and Euclidean distance:
| Model | Performance Semantic Search (6 Datasets) | Queries (GPU / CPU) per sec. |
|---|---|---|
| multi-qa-MiniLM-L6-cos-v1 | 51.83 | 18,000 / 750 |
| multi-qa-distilbert-cos-v1 | 52.83 | 7,000 / 350 |
| multi-qa-mpnet-base-cos-v1 | 57.46 | 4,000 / 170 |
MSMARCO Passage Models¶
The MSMARCO Passage Ranking Dataset contains 500k real queries from Bing search together with the relevant passages from various web sources. Given the diversity of the MSMARCO dataset, models also perform well on other domains.
Models tuned to be used with dot-product:
| Model | MSMARCO MRR@10 dev set | Performance Semantic Search (6 Datasets) | Queries (GPU / CPU) per sec. |
|---|---|---|---|
| msmarco-distilbert-base-tas-b | 34.43 | 49.25 | 7,000 / 350 |
| msmarco-distilbert-dot-v5 | 37.25 | 49.47 | 7,000 / 350 |
| msmarco-bert-base-dot-v5 | 38.08 | 52.11 | 4,000 / 170 |
These models produce normalized vectors of length 1, which can be used with dot-product, cosine-similarity and Euclidean distance:
| Model | MSMARCO MRR@10 dev set | Performance Semantic Search (6 Datasets) | Queries (GPU / CPU) per sec. |
|---|---|---|---|
| msmarco-MiniLM-L6-cos-v5 | 32.27 | 42.16 | 18,000 / 750 |
| msmarco-MiniLM-L12-cos-v5 | 32.75 | 43.89 | 11,000 / 400 |
| msmarco-distilbert-cos-v5 | 33.79 | 44.98 | 7,000 / 350 |
Multi-Lingual Models¶
The following models generate aligned vector spaces, i.e., similar inputs in different languages are mapped close in vector space. You do not need to specify the input language. Details are in our publication Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. We used the following 50+ languages: ar, bg, ca, cs, da, de, el, es, et, fa, fi, fr, fr-ca, gl, gu, he, hi, hr, hu, hy, id, it, ja, ka, ko, ku, lt, lv, mk, mn, mr, ms, my, nb, nl, pl, pt, pt, pt-br, ro, ru, sk, sl, sq, sr, sv, th, tr, uk, ur, vi, zh-cn, zh-tw.
Semantic Similarity
These models find semantically similar sentences within one language or across languages:
distiluse-base-multilingual-cased-v1: Multilingual knowledge distilled version of multilingual Universal Sentence Encoder. Supports 15 languages: Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Portuguese, Russian, Spanish, Turkish.
distiluse-base-multilingual-cased-v2: Multilingual knowledge distilled version of multilingual Universal Sentence Encoder. This version supports 50+ languages, but performs a bit weaker than the v1 model.
paraphrase-multilingual-MiniLM-L12-v2 - Multilingual version of paraphrase-MiniLM-L12-v2, trained on parallel data for 50+ languages.
paraphrase-multilingual-mpnet-base-v2 - Multilingual version of paraphrase-mpnet-base-v2, trained on parallel data for 50+ languages.
Bitext Mining
Bitext mining describes the process of finding translated sentence pairs in two languages. If this is your use-case, the following model gives the best performance:
LaBSE - LaBSE Model. Supports 109 languages. Works well for finding translation pairs in multiple languages. As detailed here, LaBSE works less well for assessing the similarity of sentence pairs that are not translations of each other.
Extending a model to new languages is easy by following the description here.
Image & Text-Models¶
The following models can embed images and text into a joint vector space. See Image Search for more details how to use for text2image-search, image2image-search, image clustering, and zero-shot image classification.
clip-ViT-B-32 - OpenAPI CLIP Model
clip-ViT-B-32-multilingual-v1 - Multilingual text encoder for the CLIP model using Multilingual Knowledge Distillation.
Other Models¶
Scientific Publications¶
SPECTER is a model trained on scientific citations and can be used to estimate the similarity of two publications. We can use it to find similar papers.
allenai-specter - Semantic Search Python Example / Semantic Search Colab Example
Natural Questions (NQ) Dataset Models¶
The following models were trained on Google’s Natural Questions dataset, a dataset with 100k real queries from Google search together with the relevant passages from Wikipedia.
nq-distilbert-base-v1: MRR10: 72.36 on NQ dev set (small)
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('nq-distilbert-base-v1')
query_embedding = model.encode('How many people live in London?')
#The passages are encoded as [ [title1, text1], [title2, text2], ...]
passage_embedding = model.encode([['London', 'London has 9,787,426 inhabitants at the 2011 census.']])
print("Similarity:", util.pytorch_cos_sim(query_embedding, passage_embedding))
You can index the passages as shown here.
Note: The NQ model doesn’t perform well. Use the above mentioned Multi-QA models to achieve the optimal performance.
DPR-Models
In Dense Passage Retrieval for Open-Domain Question Answering Karpukhin et al. trained models based on Google’s Natural Questions dataset:
facebook-dpr-ctx_encoder-single-nq-base
facebook-dpr-question_encoder-single-nq-base
They also trained models on the combination of Natural Questions, TriviaQA, WebQuestions, and CuratedTREC.
facebook-dpr-ctx_encoder-multiset-base
facebook-dpr-question_encoder-multiset-base
Note: The DPR models perform comparabily bad. Use the above mentioned Multi-QA models to achieve the optimal performance.
Average Word Embeddings Models¶
The following models apply compute the average word embedding for some well-known word embedding methods. Their computation speed is much higher than the transformer based models, but the quality of the embeddings are worse.
average_word_embeddings_glove.6B.300d
average_word_embeddings_komninos
average_word_embeddings_levy_dependency
average_word_embeddings_glove.840B.300d