metadata
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- UniHGKR-base
widget: []
UniHGKR-base
Please see github repository UniHGKR to know how to use this model.
We recommend using the sentence-transformers package to load our model and to perform embedding for paragraphs and sentences.
It maps sentences & paragraphs to a 768-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
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
Training Details
Framework Versions
- Python: 3.8.10
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- PyTorch: 2.0.0+cu118
- Accelerate: 0.34.0
- Datasets: 2.21.0
- Tokenizers: 0.19.1