bobox/custom-XLMRoBERTa-m3-pooler-step1

SentenceTransformer based on BAAI/bge-m3

This is a sentence-transformers model finetuned from BAAI/bge-m3. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: BAAI/bge-m3
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): CustomPooler(
    (ln_queries): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
    (ln_tokens): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
    (q_proj): Linear(in_features=1024, out_features=2048, bias=True)
    (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
    (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
    (o_proj): Linear(in_features=2048, out_features=1024, bias=True)
    (attn_drop): Dropout(p=0.05, inplace=False)
    (fusion_proj): Linear(in_features=4096, out_features=1024, bias=True)
    (mlp): SwiGLU(
      (gate_proj): Linear(in_features=2048, out_features=3072, bias=True)
      (up_proj): Linear(in_features=2048, out_features=3072, bias=True)
      (down_proj): Linear(in_features=3072, out_features=1024, bias=True)
      (drop): Dropout(p=0.05, inplace=False)
    )
  )
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("bobox/custom-XLMRoBERTa-m3-pooler-step1")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9497, 0.8788],
#         [0.9497, 1.0000, 0.8856],
#         [0.8788, 0.8856, 1.0000]])

Training Details

Framework Versions

• Python: 3.12.12
• Sentence Transformers: 5.3.0
• Transformers: 5.0.0
• PyTorch: 2.10.0+cu128
• Accelerate: 1.13.0
• Datasets: 4.0.0
• Tokenizers: 0.22.2

Citation

BibTeX