README.md

metadata

license: apache-2.0

This model is a merged model, using bge-small-en-v1.5, GIST-Embedding-v0 and gte-base. This model focuses on retrieval tasks while also performing well on various tasks (See experiment details below).

Usage

For retrieval tasks

from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["this is a test sentence", "this is another test sentence"]

# Prefixing for retrieval tasks
instruction = "Represent this sentence for searching relevant passages: "

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Marqo/marqo-merged-bge-gist-gte-base')
model = AutoModel.from_pretrained('Marqo/marqo-merged-bge-gist-gte-base')
model.eval()

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
encoded_input_with_prefixing = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)
    model_output_with_prefixing = model(**encoded_input_with_prefixing)
    model_output_avg = (model_output + model_output_with_prefixing) / 2
    # Perform pooling. In this case, cls pooling.
    sentence_embeddings = model_output_avg[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)

Evaluation

Models	Average	ArguAna	ClimateFEVER	CQADupstackRetrieval	DBPedia	FEVER	FiQA2018	HotpotQA	MSMARCO	NFCorpus	NQ	QuoraRetrieval	SCIDOCS	SciFact	Touche2020	TRECCOVID
Slerp (average prefixing)	53.37	63.45	31.66	42.51	42.15	86.53	43.28	73.72	39.16	37.66	54.39	88.96	22.7	76.66	21.46	76.23
Slerp (prefixing)	53.18	63.58	30.67	43.23	41.52	86.54	41.28	71.43	41.16	38.01	55.28	88.72	22.75	75.05	22.15	76.27
BGE	53.25	63.61	31.17	42.35	40.77	86.29	40.65	72.6	41.35	37.39	54.15	88.9	21.73	74.04	25.7	78.07
GIST	52.31	62.62	31.49	43.2	41.7	86.65	40.64	68.92	40.64	37.64	53.43	88.81	23.47	75.29	20.58	69.6
GTE	51.14	57.12	28.1	42.91	41.19	81.52	40.76	65.75	40.21	37.9	52.84	88.15	23.13	76.18	22.55	68.78