GISTsmall

Model Details

Model Description

• Model Type: Sentence Transformer

• Maximum Sequence Length: 512 tokens

• Output Dimensionality: 384 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': 512, 'do_lower_case': True}) with Transformer model: BertModel
  (1): Pooling({'word_embedding_dimension': 384, '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()
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Soga no Tojiko sits with a playful smile, looking directly at the viewer. Her short green hair falls over her eyes, highlighting her blush and expressive gaze. She wears a green dress with long sleeves and a high collar, along with a distinctive black hat featuring a red accent. Notably, she has a ghost tail instead of legs, showcasing her black panties and large breasts. Japanese text appears on the right side of the image.',
    "Tojiko's playful smile captivates you, her green eyes sparkling with mischief. Her unique green dress, a perfect match for her hair, hugs her curves, emphasizing her ample chest. But it's her ghostly tail that draws your attention, a mysterious feature that adds to her allure. She sits there, legs crossed, her black panties barely visible, leaving much to the imagination. The Japanese text beside her seems to whisper secrets, adding to the enigmatic charm of this playful spirit.",
    "A young woman with a similar smile sits cross-legged on a cozy couch, her long, dark hair cascading over her shoulders. She's dressed in a casual, loose-fitting shirt and jeans, a comfortable outfit for a relaxed evening. Her legs are very much human, and she wears cute, colorful socks. The atmosphere is lighthearted, and she seems ready for a fun conversation, her expression inviting and warm.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,244 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 39 tokensmean: 113.16 tokensmax: 205 tokens	min: 59 tokensmean: 104.96 tokensmax: 167 tokens	min: 39 tokensmean: 86.33 tokensmax: 151 tokens

• Samples: | anchor | positive | negative