metadata
language:
- en
- ar
- cs
- de
- es
- fr
- it
- ja
- ko
- nl
- pt
- zh
license: apache-2.0
library_name: transformers
tags:
- language
- granite
- embeddings
- multilingual
model-index:
- name: ibm-granite/granite-embedding-107m-multilingual
results:
- dataset:
type: miracl/mmteb-miracl
name: Miracl (en)
config: en
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.41176
- type: ndcg_at_10
value: 0.46682
- type: ndcg_at_100
value: 0.54326
- type: ndcg_at_1000
value: 0.56567
- type: ndcg_at_20
value: 0.50157
- type: ndcg_at_3
value: 0.41197
- type: ndcg_at_5
value: 0.42086
- type: recall_at_1
value: 0.19322
- type: recall_at_10
value: 0.57721
- type: recall_at_100
value: 0.83256
- type: recall_at_1000
value: 0.95511
- type: recall_at_20
value: 0.6757
- type: recall_at_3
value: 0.37171
- type: recall_at_5
value: 0.44695
- dataset:
type: miracl/mmteb-miracl
name: Miracl (ar)
config: ar
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.55559
- type: ndcg_at_10
value: 0.62541
- type: ndcg_at_100
value: 0.67101
- type: ndcg_at_1000
value: 0.6805
- type: ndcg_at_20
value: 0.64739
- type: ndcg_at_3
value: 0.56439
- type: ndcg_at_5
value: 0.59347
- type: recall_at_1
value: 0.37009
- type: recall_at_10
value: 0.73317
- type: recall_at_100
value: 0.90066
- type: recall_at_1000
value: 0.96272
- type: recall_at_20
value: 0.80205
- type: recall_at_3
value: 0.56903
- type: recall_at_5
value: 0.6518
- dataset:
type: miracl/mmteb-miracl
name: Miracl (bn)
config: bn
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.56691
- type: ndcg_at_10
value: 0.65484
- type: ndcg_at_100
value: 0.70142
- type: ndcg_at_1000
value: 0.70994
- type: ndcg_at_20
value: 0.67838
- type: ndcg_at_3
value: 0.5988
- type: ndcg_at_5
value: 0.62718
- type: recall_at_1
value: 0.3605
- type: recall_at_10
value: 0.76854
- type: recall_at_100
value: 0.9285
- type: recall_at_1000
value: 0.97928
- type: recall_at_20
value: 0.83667
- type: recall_at_3
value: 0.61596
- type: recall_at_5
value: 0.69766
- dataset:
type: miracl/mmteb-miracl
name: Miracl (de)
config: de
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.41967
- type: ndcg_at_10
value: 0.45141
- type: ndcg_at_100
value: 0.53461
- type: ndcg_at_1000
value: 0.55463
- type: ndcg_at_20
value: 0.49012
- type: ndcg_at_3
value: 0.39486
- type: ndcg_at_5
value: 0.41496
- type: recall_at_1
value: 0.19494
- type: recall_at_10
value: 0.53774
- type: recall_at_100
value: 0.83314
- type: recall_at_1000
value: 0.95045
- type: recall_at_20
value: 0.65659
- type: recall_at_3
value: 0.3556
- type: recall_at_5
value: 0.44448
- dataset:
type: miracl/mmteb-miracl
name: Miracl (es)
config: es
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.54475
- type: ndcg_at_10
value: 0.46593
- type: ndcg_at_100
value: 0.58079
- type: ndcg_at_1000
value: 0.60656
- type: ndcg_at_20
value: 0.51858
- type: ndcg_at_3
value: 0.4578
- type: ndcg_at_5
value: 0.44321
- type: recall_at_1
value: 0.15966
- type: recall_at_10
value: 0.49343
- type: recall_at_100
value: 0.82684
- type: recall_at_1000
value: 0.95299
- type: recall_at_20
value: 0.62367
- type: recall_at_3
value: 0.2949
- type: recall_at_5
value: 0.37983
- dataset:
type: miracl/mmteb-miracl
name: Miracl (fa)
config: fa
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.36709
- type: ndcg_at_10
value: 0.46961
- type: ndcg_at_100
value: 0.53262
- type: ndcg_at_1000
value: 0.55024
- type: ndcg_at_20
value: 0.49892
- type: ndcg_at_3
value: 0.40235
- type: ndcg_at_5
value: 0.42866
- type: recall_at_1
value: 0.22735
- type: recall_at_10
value: 0.59949
- type: recall_at_100
value: 0.83867
- type: recall_at_1000
value: 0.95007
- type: recall_at_20
value: 0.68947
- type: recall_at_3
value: 0.41781
- type: recall_at_5
value: 0.49374
- dataset:
type: miracl/mmteb-miracl
name: Miracl (fi)
config: fi
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.59245
- type: ndcg_at_10
value: 0.65551
- type: ndcg_at_100
value: 0.6967
- type: ndcg_at_1000
value: 0.70521
- type: ndcg_at_20
value: 0.67552
- type: ndcg_at_3
value: 0.58876
- type: ndcg_at_5
value: 0.61779
- type: recall_at_1
value: 0.37669
- type: recall_at_10
value: 0.76529
- type: recall_at_100
value: 0.9156
- type: recall_at_1000
value: 0.96977
- type: recall_at_20
value: 0.82685
- type: recall_at_3
value: 0.60234
- type: recall_at_5
value: 0.67135
- dataset:
type: miracl/mmteb-miracl
name: Miracl (fr)
config: fr
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.38776
- type: ndcg_at_10
value: 0.47589
- type: ndcg_at_100
value: 0.54641
- type: ndcg_at_1000
value: 0.5629
- type: ndcg_at_20
value: 0.51203
- type: ndcg_at_3
value: 0.38924
- type: ndcg_at_5
value: 0.42572
- type: recall_at_1
value: 0.22082
- type: recall_at_10
value: 0.61619
- type: recall_at_100
value: 0.87237
- type: recall_at_1000
value: 0.97449
- type: recall_at_20
value: 0.72689
- type: recall_at_3
value: 0.39527
- type: recall_at_5
value: 0.48983
- dataset:
type: miracl/mmteb-miracl
name: Miracl (hi)
config: hi
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.33143
- type: ndcg_at_10
value: 0.42084
- type: ndcg_at_100
value: 0.48647
- type: ndcg_at_1000
value: 0.50712
- type: ndcg_at_20
value: 0.45399
- type: ndcg_at_3
value: 0.34988
- type: ndcg_at_5
value: 0.37938
- type: recall_at_1
value: 0.17852
- type: recall_at_10
value: 0.55217
- type: recall_at_100
value: 0.79929
- type: recall_at_1000
value: 0.93434
- type: recall_at_20
value: 0.65231
- type: recall_at_3
value: 0.33765
- type: recall_at_5
value: 0.43828
- dataset:
type: miracl/mmteb-miracl
name: Miracl (id)
config: id
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.43854
- type: ndcg_at_10
value: 0.45459
- type: ndcg_at_100
value: 0.53643
- type: ndcg_at_1000
value: 0.56052
- type: ndcg_at_20
value: 0.48795
- type: ndcg_at_3
value: 0.41041
- type: ndcg_at_5
value: 0.42235
- type: recall_at_1
value: 0.19193
- type: recall_at_10
value: 0.5289
- type: recall_at_100
value: 0.79649
- type: recall_at_1000
value: 0.92937
- type: recall_at_20
value: 0.61813
- type: recall_at_3
value: 0.35431
- type: recall_at_5
value: 0.43348
- dataset:
type: miracl/mmteb-miracl
name: Miracl (ja)
config: ja
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.53256
- type: ndcg_at_10
value: 0.59922
- type: ndcg_at_100
value: 0.65407
- type: ndcg_at_1000
value: 0.66484
- type: ndcg_at_20
value: 0.62596
- type: ndcg_at_3
value: 0.53717
- type: ndcg_at_5
value: 0.56523
- type: recall_at_1
value: 0.34555
- type: recall_at_10
value: 0.71476
- type: recall_at_100
value: 0.91152
- type: recall_at_1000
value: 0.97728
- type: recall_at_20
value: 0.79811
- type: recall_at_3
value: 0.53482
- type: recall_at_5
value: 0.62327
- dataset:
type: miracl/mmteb-miracl
name: Miracl (ko)
config: ko
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.5493
- type: ndcg_at_10
value: 0.58413
- type: ndcg_at_100
value: 0.64374
- type: ndcg_at_1000
value: 0.65655
- type: ndcg_at_20
value: 0.61732
- type: ndcg_at_3
value: 0.53068
- type: ndcg_at_5
value: 0.55202
- type: recall_at_1
value: 0.32602
- type: recall_at_10
value: 0.68647
- type: recall_at_100
value: 0.87746
- type: recall_at_1000
value: 0.95524
- type: recall_at_20
value: 0.78089
- type: recall_at_3
value: 0.49173
- type: recall_at_5
value: 0.5827
- dataset:
type: miracl/mmteb-miracl
name: Miracl (ru)
config: ru
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.43131
- type: ndcg_at_10
value: 0.48262
- type: ndcg_at_100
value: 0.56158
- type: ndcg_at_1000
value: 0.57929
- type: ndcg_at_20
value: 0.52023
- type: ndcg_at_3
value: 0.42808
- type: ndcg_at_5
value: 0.44373
- type: recall_at_1
value: 0.22018
- type: recall_at_10
value: 0.58034
- type: recall_at_100
value: 0.84074
- type: recall_at_1000
value: 0.93938
- type: recall_at_20
value: 0.68603
- type: recall_at_3
value: 0.39307
- type: recall_at_5
value: 0.47077
- dataset:
type: miracl/mmteb-miracl
name: Miracl (sw)
config: sw
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.50415
- type: ndcg_at_10
value: 0.59111
- type: ndcg_at_100
value: 0.64312
- type: ndcg_at_1000
value: 0.65089
- type: ndcg_at_20
value: 0.61651
- type: ndcg_at_3
value: 0.5304
- type: ndcg_at_5
value: 0.56139
- type: recall_at_1
value: 0.33267
- type: recall_at_10
value: 0.72082
- type: recall_at_100
value: 0.91377
- type: recall_at_1000
value: 0.96152
- type: recall_at_20
value: 0.79943
- type: recall_at_3
value: 0.5548
- type: recall_at_5
value: 0.64302
- dataset:
type: miracl/mmteb-miracl
name: Miracl (te)
config: te
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.64372
- type: ndcg_at_10
value: 0.78175
- type: ndcg_at_100
value: 0.79523
- type: ndcg_at_1000
value: 0.79774
- type: ndcg_at_20
value: 0.78826
- type: ndcg_at_3
value: 0.74856
- type: ndcg_at_5
value: 0.77128
- type: recall_at_1
value: 0.63688
- type: recall_at_10
value: 0.90358
- type: recall_at_100
value: 0.96558
- type: recall_at_1000
value: 0.9847
- type: recall_at_20
value: 0.92834
- type: recall_at_3
value: 0.81804
- type: recall_at_5
value: 0.87198
- dataset:
type: miracl/mmteb-miracl
name: Miracl (th)
config: th
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.65484
- type: ndcg_at_10
value: 0.71774
- type: ndcg_at_100
value: 0.75362
- type: ndcg_at_1000
value: 0.75898
- type: ndcg_at_20
value: 0.73709
- type: ndcg_at_3
value: 0.66199
- type: ndcg_at_5
value: 0.68451
- type: recall_at_1
value: 0.45911
- type: recall_at_10
value: 0.82619
- type: recall_at_100
value: 0.95515
- type: recall_at_1000
value: 0.98854
- type: recall_at_20
value: 0.88447
- type: recall_at_3
value: 0.67437
- type: recall_at_5
value: 0.73786
- dataset:
type: miracl/mmteb-miracl
name: Miracl (yo)
config: yo
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.46218
- type: ndcg_at_10
value: 0.64685
- type: ndcg_at_100
value: 0.66941
- type: ndcg_at_1000
value: 0.67361
- type: ndcg_at_20
value: 0.65548
- type: ndcg_at_3
value: 0.57609
- type: ndcg_at_5
value: 0.62021
- type: recall_at_1
value: 0.42787
- type: recall_at_10
value: 0.82913
- type: recall_at_100
value: 0.93277
- type: recall_at_1000
value: 0.96499
- type: recall_at_20
value: 0.85994
- type: recall_at_3
value: 0.65406
- type: recall_at_5
value: 0.7542
- dataset:
type: miracl/mmteb-miracl
name: Miracl (zh)
config: zh
split: dev
task:
type: Retrieval
metrics:
- type: ndcg_at_1
value: 0.41985
- type: ndcg_at_10
value: 0.4837
- type: ndcg_at_100
value: 0.55961
- type: ndcg_at_1000
value: 0.5762
- type: ndcg_at_20
value: 0.51595
- type: ndcg_at_3
value: 0.42094
- type: ndcg_at_5
value: 0.44273
- type: recall_at_1
value: 0.21446
- type: recall_at_10
value: 0.59695
- type: recall_at_100
value: 0.87388
- type: recall_at_1000
value: 0.96833
- type: recall_at_20
value: 0.69252
- type: recall_at_3
value: 0.40377
- type: recall_at_5
value: 0.4903
pipeline_tag: sentence-similarity
Granite-Embedding-107m-multilingual
Model Summary: Granite-Embedding-107M-Multilingual is a 107M parameter dense biencoder embedding model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384 and is trained using a combination of open source relevance-pair datasets with permissive, enterprise-friendly license, and IBM collected and generated datasets. This model is developed using contrastive finetuning, knowledge distillation and model merging for improved performance.
- Developers: Granite Embedding Team, IBM
- GitHub Repository: ibm-granite/granite-embedding-models
- Website: Granite Docs
- Paper: Coming Soon
- Release Date: December 18th, 2024
- License: Apache 2.0
Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite-Embedding-107M-Multilingual for languages beyond these 12 languages.
Intended use: The model is designed to produce fixed length vector representations for a given text, which can be used for text similarity, retrieval, and search applications.
Usage with Sentence Transformers: The model is compatible with SentenceTransformer library and is very easy to use:
First, install the sentence transformers library
pip install sentence_transformers
The model can then be used to encode pairs of text and find the similarity between their representations
from sentence_transformers import SentenceTransformer, util
model_path = "ibm-granite/granite-embedding-107m-multilingual"
# Load the Sentence Transformer model
model = SentenceTransformer(model_path)
input_queries = [
' Who made the song My achy breaky heart? ',
'summit define'
]
input_passages = [
"Achy Breaky Heart is a country song written by Don Von Tress. Originally titled Don't Tell My Heart and performed by The Marcy Brothers in 1991. ",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
# encode queries and passages
query_embeddings = model.encode(input_queries)
passage_embeddings = model.encode(input_passages)
# calculate cosine similarity
print(util.cos_sim(query_embeddings, passage_embeddings))
Usage with Huggingface Transformers: This is a simple example of how to use the Granite-Embedding-107m-Multilingual model with the Transformers library and PyTorch.
First, install the required libraries
pip install transformers torch
The model can then be used to encode pairs of text
import torch
from transformers import AutoModel, AutoTokenizer
model_path = "ibm-granite/granite-embedding-107m-multilingual"
# Load the model and tokenizer
model = AutoModel.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model.eval()
input_queries = [
' Who made the song My achy breaky heart? ',
'summit define'
]
# tokenize inputs
tokenized_queries = tokenizer(input_queries, padding=True, truncation=True, return_tensors='pt')
# encode queries
with torch.no_grad():
# Queries
model_output = model(**tokenized_queries)
# Perform pooling. granite-embedding-107m-multilingual uses CLS Pooling
query_embeddings = model_output[0][:, 0]
# normalize the embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1)
Evaluation: The average performance of the Granite-Embedding-107M-Multilingual on Multilingual Miracl (across 18 langauges), Mintaka Retrieval (across 8 languages) and MTEB Retrieval for English (across 15 tasks), German (across 4 tasks), Spanish (across 2 tasks), Frenc (across 5 tasks), Japanese (across 2 tasks), Arabic (1 task), Korean (1 task) and Chinese (across 8 tasks) is reported below. Granite-Embedding-107M-Multilingual is twice as fast as other models with similar embedding dimensions.
| Model | Paramters (M) | Embedding Dimension | Miracl (18) | Mintaka Retrieval (8) | MTEB English (15) | MTEB German (4) | MTEB Spanish (2) | MTEB French (5) | MTEB Japanese (2) | MTEB Arabic (1) | MTEB Korean (1) | MTEB Chinese (8) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| granite-embedding-107m-multilingual | 107 | 384 | 55.9 | 22.6 | 45.3 | 70.3 | 48.7 | 51.1 | 59.0 | 63.2 | 70.5 | 40.8 |
Model Architecture: Granite-Embedding-107m-Multilingual is based on an encoder-only XLM-RoBERTa like transformer architecture, trained internally at IBM Research.
| Model | granite-embedding-30m-english | granite-embedding-125m-english | granite-embedding-107m-multilingual | granite-embedding-278m-multilingual |
|---|---|---|---|---|
| Embedding size | 384 | 768 | 384 | 768 |
| Number of layers | 6 | 12 | 6 | 12 |
| Number of attention heads | 12 | 12 | 12 | 12 |
| Intermediate size | 1536 | 3072 | 1536 | 3072 |
| Activation Function | GeLU | GeLU | GeLU | GeLU |
| Vocabulary Size | 50265 | 50265 | 250002 | 250002 |
| Max. Sequence Length | 512 | 512 | 512 | 512 |
| # Parameters | 30M | 125M | 107M | 278M |
Training Data: Overall, the training data consists of four key sources: (1) unsupervised title-body paired data scraped from the web, (2) publicly available paired with permissive, enterprise-friendly license, (3) IBM-internal paired data targetting specific technical domains, and (4) IBM-generated synthetic data. The data is listed below:
| Dataset | Num. Pairs |
|---|---|
| Multilingual MC4 | 52,823,484 |
| Multilingual Webhose | 12,369,322 |
| English Wikipedia | 20,745,403 |
| Multilingual Wikimedia | 2,911,090 |
| Miracl Corpus (Title-Body) | 10,120,398 |
| Stack Exchange Duplicate questions (titles) | 304,525 |
| Stack Exchange Duplicate questions (titles) | 304,525 |
| Stack Exchange Duplicate questions (bodies) | 250,519 |
| Machine Translations of Stack Exchange Duplicate questions (titles) | 187,195 |
| Stack Exchange (Title, Answer) pairs | 4,067,139 |
| Stack Exchange (Title, Body) pairs | 23,978,013 |
| Stack Exchange (Title, Body) pairs | 23,978,013 |
| Machine Translations of Stack Exchange (Title+Body, Answer) pairs | 1,827,15 |
| SearchQA | 582,261 |
| S2ORC (Title, Abstract) | 41,769,185 |
| WikiAnswers Duplicate question pairs | 77,427,422 |
| CCNews | 614,664 |
| XSum | 226,711 |
| SimpleWiki | 102,225 |
| Machine Translated Cross Lingual Parallel Corpora | 28,376,115 |
| SPECTER citation triplets | 684,100 |
| Machine Translations of SPECTER citation triplets | 4,104,600 |
| Natural Questions (NQ) | 100,231 |
| SQuAD2.0 | 87,599 |
| HotpotQA | 85,000 |
| Fever | 109,810 |
| PubMed | 20,000,000 |
| Multilingual Miracl Triples | 81,409 |
| Multilingual MrTydi Triples | 48,715 |
| Sadeeem Question Asnwering | 4,037 |
| DBPedia Title-Body Pairs | 4,635,922 |
| Synthetic: English Query-Wikipedia Passage | 1,879,093 |
| Synthetic: English Fact Verification | 9,888 |
| Synthetic: Multilingual Query-Wikipedia Passage | 300,266 |
| Synthetic: Multilingual News Summaries | 37,489 |
| IBM Internal Triples | 40,290 |
| IBM Internal Title-Body Pairs | 1,524,586 |
Notably, we do not use the popular MS-MARCO retrieval dataset in our training corpus due to its non-commercial license, while other open-source models train on this dataset due to its high quality.
Infrastructure: We train Granite Embedding Models using IBM's computing cluster, Cognitive Compute Cluster, which is outfitted with NVIDIA A100 80gb GPUs. This cluster provides a scalable and efficient infrastructure for training our models over multiple GPUs.
Ethical Considerations and Limitations: The data used to train the base language model was filtered to remove text containing hate, abuse, and profanity. Granite-Embedding-278m-Multilingual is trained only for English texts, and has a context length of 512 tokens (longer texts will be truncated to this size).
Resources
- ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite
- 📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
- 💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources