---
license: mit
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- gte
- mteb
model-index:
- name: gte-micro-v4
  results:
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (en)
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 71.83582089552239
    - type: ap
      value: 34.436093320979126
    - type: f1
      value: 65.82844954638102
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_polarity
      name: MTEB AmazonPolarityClassification
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 80.03957500000001
    - type: ap
      value: 74.4510899901909
    - type: f1
      value: 79.98034714963279
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (en)
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 39.754
    - type: f1
      value: 39.423135672769796
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-p2p
      name: MTEB ArxivClusteringP2P
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 42.85928858083004
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-s2s
      name: MTEB ArxivClusteringS2S
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 32.475201371814784
  - task:
      type: Reranking
    dataset:
      type: mteb/askubuntudupquestions-reranking
      name: MTEB AskUbuntuDupQuestions
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 58.01141755339977
    - type: mrr
      value: 71.70821791320407
  - task:
      type: Classification
    dataset:
      type: mteb/banking77
      name: MTEB Banking77Classification
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 80.9220779220779
    - type: f1
      value: 80.86851039874094
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-p2p
      name: MTEB BiorxivClusteringP2P
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 36.82555236565894
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-s2s
      name: MTEB BiorxivClusteringS2S
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 29.243444611175995
  - task:
      type: Classification
    dataset:
      type: mteb/emotion
      name: MTEB EmotionClassification
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 44.87500000000001
    - type: f1
      value: 39.78455417008123
  - task:
      type: Classification
    dataset:
      type: mteb/imdb
      name: MTEB ImdbClassification
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 71.9568
    - type: ap
      value: 65.91179027501194
    - type: f1
      value: 71.85575290323182
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (en)
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 90.87323301413589
    - type: f1
      value: 90.45433994230181
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (en)
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 68.53169174646602
    - type: f1
      value: 50.49367676485481
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (en)
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 69.11230665770007
    - type: f1
      value: 66.9035022957204
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (en)
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 74.15601882985877
    - type: f1
      value: 74.059011768806
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-p2p
      name: MTEB MedrxivClusteringP2P
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 32.551619758274406
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-s2s
      name: MTEB MedrxivClusteringS2S
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 30.80210958999942
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering
      name: MTEB RedditClustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 48.27542501963987
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering-p2p
      name: MTEB RedditClusteringP2P
      config: default
      split: test
      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
    metrics:
    - type: v_measure
      value: 53.55942763860501
  - task:
      type: PairClassification
    dataset:
      type: mteb/sprintduplicatequestions-pairclassification
      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.82673267326733
    - type: cos_sim_ap
      value: 95.53621808930455
    - type: cos_sim_f1
      value: 91.19275289380975
    - type: cos_sim_precision
      value: 91.7933130699088
    - type: cos_sim_recall
      value: 90.60000000000001
    - type: dot_accuracy
      value: 99.75445544554455
    - type: dot_ap
      value: 92.76410342229411
    - type: dot_f1
      value: 87.50612444879961
    - type: dot_precision
      value: 85.78290105667628
    - type: dot_recall
      value: 89.3
    - type: euclidean_accuracy
      value: 99.82673267326733
    - type: euclidean_ap
      value: 95.46124795179632
    - type: euclidean_f1
      value: 91.01181304571135
    - type: euclidean_precision
      value: 93.55860612460401
    - type: euclidean_recall
      value: 88.6
    - type: manhattan_accuracy
      value: 99.82871287128712
    - type: manhattan_ap
      value: 95.51436288466519
    - type: manhattan_f1
      value: 91.11891620672353
    - type: manhattan_precision
      value: 91.44008056394763
    - type: manhattan_recall
      value: 90.8
    - type: max_accuracy
      value: 99.82871287128712
    - type: max_ap
      value: 95.53621808930455
    - type: max_f1
      value: 91.19275289380975
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering
      name: MTEB StackExchangeClustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 55.0721745308552
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering-p2p
      name: MTEB StackExchangeClusteringP2P
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 31.91639764792279
  - task:
      type: Classification
    dataset:
      type: mteb/toxic_conversations_50k
      name: MTEB ToxicConversationsClassification
      config: default
      split: test
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
    metrics:
    - type: accuracy
      value: 66.0402
    - type: ap
      value: 12.106715125588833
    - type: f1
      value: 50.67443088623853
  - task:
      type: Classification
    dataset:
      type: mteb/tweet_sentiment_extraction
      name: MTEB TweetSentimentExtractionClassification
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 59.42840973401245
    - type: f1
      value: 59.813350770208665
  - task:
      type: Clustering
    dataset:
      type: mteb/twentynewsgroups-clustering
      name: MTEB TwentyNewsgroupsClustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 41.37273187829312
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 84.10919711509806
    - type: cos_sim_ap
      value: 67.55255054010537
    - type: cos_sim_f1
      value: 64.22774378823823
    - type: cos_sim_precision
      value: 60.9623133443944
    - type: cos_sim_recall
      value: 67.86279683377309
    - type: dot_accuracy
      value: 80.62228050306967
    - type: dot_ap
      value: 54.81480289413879
    - type: dot_f1
      value: 54.22550997534184
    - type: dot_precision
      value: 47.13561964146532
    - type: dot_recall
      value: 63.82585751978892
    - type: euclidean_accuracy
      value: 84.04363116170948
    - type: euclidean_ap
      value: 67.77652401372912
    - type: euclidean_f1
      value: 64.46694460988684
    - type: euclidean_precision
      value: 58.762214983713356
    - type: euclidean_recall
      value: 71.39841688654354
    - type: manhattan_accuracy
      value: 83.94230196101806
    - type: manhattan_ap
      value: 67.419155052755
    - type: manhattan_f1
      value: 64.15049692380501
    - type: manhattan_precision
      value: 58.151008151008156
    - type: manhattan_recall
      value: 71.53034300791556
    - type: max_accuracy
      value: 84.10919711509806
    - type: max_ap
      value: 67.77652401372912
    - type: max_f1
      value: 64.46694460988684
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.25823728024217
    - type: cos_sim_ap
      value: 84.67785320317506
    - type: cos_sim_f1
      value: 76.67701296330108
    - type: cos_sim_precision
      value: 72.92491491282907
    - type: cos_sim_recall
      value: 80.83615645210965
    - type: dot_accuracy
      value: 84.63344588038964
    - type: dot_ap
      value: 75.25182203961072
    - type: dot_f1
      value: 70.35217601881962
    - type: dot_precision
      value: 63.87737152908657
    - type: dot_recall
      value: 78.28765013858947
    - type: euclidean_accuracy
      value: 88.2504754142896
    - type: euclidean_ap
      value: 84.68882859374924
    - type: euclidean_f1
      value: 76.69534508021188
    - type: euclidean_precision
      value: 74.89177489177489
    - type: euclidean_recall
      value: 78.58792731752386
    - type: manhattan_accuracy
      value: 88.26211821321846
    - type: manhattan_ap
      value: 84.60061548046698
    - type: manhattan_f1
      value: 76.63928519959647
    - type: manhattan_precision
      value: 72.02058504875406
    - type: manhattan_recall
      value: 81.89097628580228
    - type: max_accuracy
      value: 88.26211821321846
    - type: max_ap
      value: 84.68882859374924
    - type: max_f1
      value: 76.69534508021188
---
# gte-micro-v4

This is a distill of [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny).

## Intended purpose

<span style="color:blue">This model is designed for use in semantic-autocomplete ([click here for demo](https://mihaiii.github.io/semantic-autocomplete/)).</span>

## Usage (Sentence-Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny))

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('Mihaiii/gte-micro-v4')
embeddings = model.encode(sentences)
print(embeddings)
```



## Usage (HuggingFace Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny))
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


#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('Mihaiii/gte-micro-v4')
model = AutoModel.from_pretrained('Mihaiii/gte-micro-v4')

# 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. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

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

### Limitation (same as [gte-small](https://huggingface.co/thenlper/gte-small))
This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.