metadata

pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
datasets:
  - HaifaCLGroup/KnessetCorpus
language:
  - he
base_model:
  - intfloat/multilingual-e5-large

Knesset-multi-e5-large

This is a sentence-transformers model. It maps sentences and paragraphs to a 1024-dimensional dense vector space and can be used for tasks like clustering or semantic search.

Knesset-multi-e5-large is based on the intfloat/multilingual-e5-large model. The transformer encoder has been fine-tuned on Knesset data to better capture legislative and parliamentary language.

Usage (Sentence-Transformers)

Using this model is straightforward if you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["זה משפט ראשון לדוגמה", "זה המשפט השני"]

model = SentenceTransformer('GiliGold/Knesset-multi-e5-large')
embeddings = model.encode(sentences)
print(embeddings)

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Normalize()
)

Additional Details

Base Model: intfloat/multilingual-e5-large
Fine-Tuning Data: Knesset data
Key Modifications: The encoder part has been fine-tuned on Knesset data to enhance performance for tasks involving legislative and parliamentary content. The original pooling and normalization layers have been retained to ensure that the model's embeddings remain consistent with the architecture of the base model.

Citing & Authors

TBD