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