metadata

language: []
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
widget: []
pipeline_tag: sentence-similarity
model-index:
  - name: SentenceTransformer
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: pearson_cosine
            value: 0.841929698952355
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7942182059969294
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8295844701949633
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7967029159438351
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8302175995746677
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7974109108557925
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.8266168802012493
            name: Pearson Dot
          - type: spearman_dot
            value: 0.7757964222446627
            name: Spearman Dot
          - type: pearson_max
            value: 0.841929698952355
            name: Pearson Max
          - type: spearman_max
            value: 0.7974109108557925
            name: Spearman Max

SentenceTransformer

This is a sentence-transformers model trained. 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

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': 512, 'do_lower_case': False}) with Transformer model: LukeModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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("pkshatech/GLuCoSE-base-ja-v2")
# 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, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8419
spearman_cosine	0.7942
pearson_manhattan	0.8296
spearman_manhattan	0.7967
pearson_euclidean	0.8302
spearman_euclidean	0.7974
pearson_dot	0.8266
spearman_dot	0.7758
pearson_max	0.8419
spearman_max	0.7974

Training Details

Training Logs

Epoch	Step	spearman_cosine
0	0	0.7942

Framework Versions

Python: 3.10.13
Sentence Transformers: 3.0.0
Transformers: 4.41.2
PyTorch: 2.3.1+cu118
Accelerate: 0.30.1
Datasets: 2.19.2
Tokenizers: 0.19.1

pkshatech
/

GLuCoSE-base-ja-v2