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Cataloged from ibm-granite/granite-embedding-125m-english
All models by ibm-granite →News: Granite Embedding R2 models with 8192 context length released.
Model Summary: Granite-Embedding-125m-English is a 125M 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 768. Compared to most other open-source models, this model was only trained using open-source relevance-pair datasets with permissive, enterprise-friendly license, plus IBM collected and generated datasets. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. This model is developed using retrieval oriented pretraining, contrastive finetuning and knowledge distillation.
Supported Languages: English.
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
The model can then be used to encode pairs of text and find the similarity between their representations
Usage with Huggingface Transformers: This is a simple example of how to use the Granite-Embedding-125m-English model with the Transformers library and PyTorch.
First, install the required libraries
The model can then be used to encode pairs of text
Evaluation:
The performance of the Granite-Embedding-125M-English model on MTEB Retrieval (i.e., BEIR) and code retrieval (CoIR) benchmarks is reported below.
Model Architecture: Granite-Embedding-125m-English is based on an encoder-only RoBERTa like transformer architecture, trained internally at IBM Research.
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:
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-125m-English is trained only for English texts, and has a context length of 512 tokens (longer texts will be truncated to this size).
Resources
pip install sentence_transformersfrom sentence_transformers import SentenceTransformer, util
model_path = "ibm-granite/granite-embedding-125m-english"# Load the Sentence Transformer modelmodel = 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 passagesquery_embeddings = model.encode(input_queries)passage_embeddings = model.encode(input_passages)
# calculate cosine similarityprint(util.cos_sim(query_embeddings, passage_embeddings))pip install transformers torchimport torchfrom transformers import AutoModel, AutoTokenizer
model_path = "ibm-granite/granite-embedding-125m-english"
# Load the model and tokenizermodel = 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 inputstokenized_queries = tokenizer(input_queries, padding=True, truncation=True, return_tensors='pt')
# encode querieswith torch.no_grad(): # Queries model_output = model(**tokenized_queries) # Perform pooling. granite-embedding-125m-english uses CLS Pooling query_embeddings = model_output[0][:, 0]
# normalize the embeddingsquery_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1)