semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more

---
language: en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- s2orc
- flax-sentence-embeddings/stackexchange_xml
- ms_marco
- gooaq
- yahoo_answers_topics
- code_search_net
- search_qa
- eli5
- snli
- multi_nli
- wikihow
- natural_questions
- trivia_qa
- embedding-data/sentence-compression
- embedding-data/flickr30k-captions
- embedding-data/altlex
- embedding-data/simple-wiki
- embedding-data/QQP
- embedding-data/SPECTER
- embedding-data/PAQ_pairs
- embedding-data/WikiAnswers
pipeline_tag: sentence-similarity
---

# all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.

## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)
```

## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F

#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)

print("Sentence embeddings:")
print(sentence_embeddings)
```

## Evaluation Results

For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2)

------

## Background

The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised 
contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 
1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.

We developed this model during the 
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), 
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.

## Intended uses

Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures 
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.

By default, input text longer than 256 word pieces is truncated.

## Training procedure

### Pre-training 

We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.

### Fine-tuning 

We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.

#### Hyper parameters

We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.

#### Training data

We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.

| Dataset                                                  | Paper                                    | Number of training tuples  |
|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs  | - | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs  | - | 21,396,559 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs  | - | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | 
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| **Total** | | **1,170,060,424** |

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HomeNewsBusinessMarketsStock Market LIVE Updates: Nifty below 22,900, Sensex down 250 points; all sectors in the red

Trending Topics

Sensex TodayABB India Share PriceZen Technologies Share PricePersistent Systems share Price Anant Raj Share PriceBharti Airtel Block Deal

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Stock Market LIVE Updates: Nifty below 22,900, Sensex down 250 points; all sectors in the red

Live now

Sensex Today | Stock Market LIVE Updates: All the sectoral indices are trading in the red. BSE Midcap index down 1 percent and smallcap index down 2 percent. Godfrey Phillips, ABB India, Bharti Airtel, HDFC Bank, TCS are among most active shares on the NSE.

February 18, 2025 / 12:23 IST

Stock Market Today

Moneycontrol.com

Index Prices Change Change% Sensex 75,735.93 -260.93 -0.34% Nifty 50 22,859.40 -100.10 -0.44% Nifty Bank 49,015.45 -243.45 -0.49%

Nifty 50 22,859.40 -100.10 (-0.44%)

Tue, Feb 18, 2025

1D

5D

1M

3M

6M

1Y

2Y

Biggest Gainer Prices Change Change% Tech Mahindra 1,685.00 20.05 +1.20%

Biggest Loser Prices Change Change% Trent 4,935.70 -160.75 -3.15%

Best Sector Prices Change Change% Nifty IT 41164.70 91.50 +0.22%

Worst Sector Prices Change Change% Nifty Metal 8148.30 -101.90 -1.24%

February 18, 2025 / 12:22 IST

Sensex Today | BSE Realty index slips on third straight session

BSE Realty Top Stock Losers (Intra-day)

Company CMP Chg(%) Volume Anant Raj 494.95 -4.63 99.40k Prestige Estate 1,201.70 -3.24 26.22k Sobha 1,116.75 -2.17 8.07k SignatureGlobal 1,102.65 -2.01 17.43k Oberoi Realty 1,563.90 -1.65 2.22k DLF 664.45 -1.54 43.77k Godrej Prop 1,964.15 -0.61 9.94k Brigade Ent 981.70 -0.28 2.17k

February 18, 2025 / 12:20 IST

Stock Market LIVE Updates | Gujarat Toolroom, Kothari Products shares trade ex-bonus

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February 18, 2025 / 12:18 IST

Stock Market LIVE Updates | Zomato unveils AI customer support platform Nugget

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The food delivery giant has introduced Nugget, an AI-native, no-code customer support platform. Nugget is the first product from Zomato Labs. Zomato is now opening Nugget for businesses worldwide.

February 18, 2025 / 12:15 IST

Stock Market LIVE Updates | One 97 Communications arm in partnership with SBI Mutual Fund

The company's subsidiary, Paytm Services, has signed a partnership with SBI Mutual Fund to launch the JanNivesh Rs 250 SIP, in line with the Government of India’s vision for a Viksit Bharat, enabling financial inclusion and empowering citizens to invest in their future.

February 18, 2025 / 12:13 IST

Markets@12 | Sensex down 280 points, Nifty at 22850

The Sensex was down 287.93 points or 0.38 percent at 75,708.93, and the Nifty was down 104.65 points or 0.46 percent at 22,854.85. About 749 shares advanced, 2690 shares declined, and 98 shares unchanged.

Advance/Decline (Intra-day)

SENSEX

;75,731.59 -265.27(-0.35%)

23

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February 18, 2025 / 12:10 IST

Sensex Today | BEML, IIFL Finance, Tata Teleservices, among others hit 52-week low

BSE 500 Stocks touched 52-week Low

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Company 52-Week Low Day’s Low CMP Swan Energy 446.40 446.40 412.45 Anupam Rasayan 683.90 683.90 605.85 Sun Pharma Adv 136.85 136.85 129.55 Natco Pharma 883.25 883.25 828.50 Jupiter Wagons 312.35 312.35 292.50 Mishra Dhatu 266.15 266.15 250.75 Ircon Internati 154.90 154.90 147.75 Welspun Living 121.50 121.50 115.30 BEML 2696.00 2696.00 2,576.70 Sheela Foam 774.85 774.85 736.60 MMTC Ltd 56.51 56.51 54.13 JBM Auto 605.70 605.70 565.70 Vedant Fashions 848.45 848.45 818.35 Carborundum 964.70 964.70 941.00 IIFL Finance 317.55 317.55 303.10 Olectra Greente 1159.75 1159.75 1,103.40 TataTeleservice 65.25 65.25 61.32 Route 1089.35 1089.35 1,030.95 RHI Magnesita 414.00 414.00 384.55 Shree Renuka 30.25 30.25 28.88

February 18, 2025 / 12:09 IST

Stock Market LIVE Updates | Allcargo Terminals' CFS volumes increases by 11%

Container freight station (CFS) volumes in January 2025 increased by 11% YoY and 4% MoM to 53.8 ‘000 TEUs, driven by exports.

February 18, 2025 / 12:05 IST

Sensex Today | Gems & Jewellery exports decline 7%, imports down 37.83% in Jan 2025

As per the data released by the Gems and Jewellery Export Promotion Council (GJEPC), the apex body for the Gems & Jewellery Industry in India, the overall gross exports of Gems & Jewellery in January 2025 stood at USD 2237.14 million (Rs. 19302.280 crores), showing a decline of 7.01% compared to USD 2405.78 million (Rs. 19995.66 crores) for the same period last year. This is due to the economic uncertainty looming in the market on the back of tariff threats by Donald Trump right after his arrival in the power again.

The overall imports of Gems & Jewellery for January 2025 stood at USD 1421.61 million (Rs. 12269.41 crores) showing a decline of 37.83% as compared to USD 2286.55 million (Rs. 19008.4 crores) for the same period last year.

February 18, 2025 / 12:03 IST

Sensex Today | Nifty Auto index extends losses on seventh day

Nifty Auto Top Stock Losers (Intra-day)

Company CMP Chg(%) Volume Bharat Forge 1,055.40 -2.52 236.87k Ashok Leyland 218.14 -2.45 3.32m M&M 2,772.30 -2.11 1.04m Exide Ind 355.05 -1.87 568.94k Balkrishna Ind 2,625.50 -1.69 29.25k Apollo Tyres 410.15 -1.48 285.32k Tata Motors 676.70 -1.44 4.06m Bajaj Auto 8,422.00 -1.35 78.54k TVS Motor 2,342.20 -0.99 228.96k MRF 109,088.00 -0.81 1.48k Hero Motocorp 3,845.25 -0.8 204.33k Eicher Motors 4,716.50 -0.6 87.17k Bosch 26,850.00 -0.23 3.62k

February 18, 2025 / 12:01 IST

Stock Market LIVE Updates | TVS Motor Company unveils new 2025 TVS Ronin

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February 18, 2025 / 12:00 IST

Stock Market LIVE Updates | ABB India launching 'flame proof motors' in India

February 18, 2025 / 11:55 IST

Global Markets | Asian Markets trade higher; Kospi up 0.6%, Hang Seng up 1%

February 18, 2025 / 11:49 IST

Stock Market LIVE Updates | Adani Group stocks trade mixed; Adani Wilmar up 4%, Adani Energy down 4%

Adani Group Top Stock Gainers (Intra-day) (BSE)

Company CMP Chg(%) Volume Adani Wilmar 245.70 4.24 69.60k Adani Green Ene 899.80 0.94 252.49k Adani Total Gas 578.05 0.65 26.50k Adani Power 490.80 0.10 249.32k

Adani Group Top Stock Losers (Intra-day) (BSE)

Company CMP Chg(%) Volume Adani Energy 674.15 -3.98 128.16k ACC 1,850.80 -3.16 16.14k NDTV 121.50 -2.37 5.59k Sanghi Ind 53.91 -1.52 4.15k Adani Enterpris 2,192.90 -1.46 63.80k Ambuja Cements 483.00 -1.19 57.92k Adani Ports 1,074.00 -0.78 29.29k

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