E5 Large Finetune Word

🚀 SentenceTransformer based on intfloat/multilingual-e5-large

This is a fine - tuned model from intfloat/multilingual - e5 - large using sentence - transformers. It maps sentences and paragraphs to a 1024 - dimensional dense vector space and can be applied in semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and other tasks.

📚 Documentation

✨ Features

• Model Type: Sentence Transformer
• Base model: intfloat/multilingual - e5 - large
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity

📦 Installation

First, install the Sentence Transformers library:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("meandyou200175/e5_large_finetune_word")
# Run inference
sentences = [
    'A long appendage protruding from the lower back. Often covered in fur or scales. A common feature of animal girls.',
    'tail',
    'stomach day',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

📊 Evaluation

Information Retrieval

Evaluated with InformationRetrievalEvaluator

🔧 Technical Details

Training Dataset

Unnamed Dataset

• Size: 10,356 training samples
• Columns: query and positive
• Approximate statistics based on the first 1000 samples:

  	query	positive
  type	string	string
  details	min: 3 tokens mean: 36.54 tokens max: 177 tokens	min: 3 tokens mean: 5.3 tokens max: 13 tokens

• Samples:

  query	positive
  Eyewear shaped like a semicircle.	semi - circular eyewear
  A handheld electric appliance used for drying and styling hair.	hair dryer
  When one breast is exposed while the other remains covered or confined by clothing. See breasts out for when both breasts are exposed.	one breast out

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Evaluation Dataset

word_embedding

• Dataset: word_embedding at af76b11
• Size: 1,036 evaluation samples
• Columns: query and positive
• Approximate statistics based on the first 1000 samples:

  	query	positive
  type	string	string
  details	min: 4 tokens mean: 35.89 tokens max: 164 tokens	min: 3 tokens mean: 5.38 tokens max: 14 tokens

• Samples:

  query	positive
  A machine that manipulates data according to a list of instructions. The ability to store and execute lists of instructions called programs make computers extremely versatile. On Danbooru's images they are most often used for drawing, playing games and accessing the internet.	computer
  A playing card with two clubs.	two of clubs
  Yebisu (ヱビス, Ebisu) is a beer produced by Sapporo Breweries. It is one of the most popular beers in Japan.	Yebisu beer

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: 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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)