FINGU-AI/Fingu-instruct-3

SentenceTransformer based on dunzhang/stella_en_1.5B_v5

This is a sentence-transformers model finetuned from dunzhang/stella_en_1.5B_v5. It maps sentences & paragraphs to a 1024-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
• Base model: dunzhang/stella_en_1.5B_v5
• Maximum Sequence Length: 8096 tokens
• Output Dimensionality: 1024 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': 8096, 'do_lower_case': False}) with Transformer model: Qwen2Model 
  (1): Pooling({'word_embedding_dimension': 1536, '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): Dense({'in_features': 1536, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
)

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 = [
    'Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: Ahu A Umi Heiau',
    'Ahu A ʻ Umi Heiau means "shrine at the temple of ʻ Umi" in the Hawaiian Language.',
    'The digit ratio is the ratio of the lengths of different digits or fingers typically measured from the midpoint of bottom crease ( where the finger joins the hand ) to the tip of the finger . It has been suggested by some scientists that the ratio of two digits in particular , the 2nd ( index finger ) and 4th ( ring finger ) , is affected by exposure to androgens , e.g. , testosterone while in the uterus and that this 2D :4 D ratio can be considered a crude measure for prenatal androgen exposure , with lower 2D :4 D ratios pointing to higher prenatal androgen exposure . The 2D :4 D ratio is calculated by dividing the length of the index finger of a given hand by the length of the ring finger of the same hand . A longer index finger will result in a ratio higher than 1 , while a longer ring finger will result in a ratio lower than 1 .   The 2D :4 D digit ratio is sexually dimorphic : although the second digit is typically shorter in both females and males , the difference between the lengths of the two digits is greater in males than in females .   A number of studies have shown a correlation between the 2D :4 D digit ratio and various physical and behavioral traits .',
]
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]

Training Logs

Epoch	Step	Training Loss	retrival loss
0.6466	500	0.0424	0.0060
1.2932	1000	0.0073	0.0040
1.9399	1500	0.0029	0.0039