Nirman
TaylorAI

TaylorAI/bge-micro-v2

pipelinetag: sentence-similarity - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb - name: bgemicro r...

Model Documentation

bge-micro-v2



This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Distilled in a 2-step training process (bge-micro was step 1) from BAAI/bge-small-en-v1.5.

Usage (Sentence-Transformers)



Using this model becomes easy when you have sentence-transformers 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('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)




Usage (HuggingFace Transformers)

Without sentence-transformers, 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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

    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)




    Evaluation Results





    For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net



    Full Model Architecture

    
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)


    Citing & Authors



    Files & Weights

    FilenameSizeAction
    model.safetensors 0.03 GB
    pytorch_model.bin 0.03 GB