SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/all-MiniLM-L6-v2
• Maximum Sequence Length: 256 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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()
)

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

Unnamed Dataset

• Size: 648 training samples
• Columns: sentence_0, sentence_1, sentence_2, sentence_3, sentence_4, and label
• Approximate statistics based on the first 648 samples:

  	sentence_0	sentence_1	sentence_2	sentence_3	sentence_4	label
  type	string	string	string	string	string	float
  details	min: 5 tokens mean: 6.95 tokens max: 12 tokens	min: 15 tokens mean: 26.94 tokens max: 42 tokens	min: 15 tokens mean: 26.78 tokens max: 42 tokens	min: 15 tokens mean: 26.75 tokens max: 42 tokens	min: 15 tokens mean: 26.9 tokens max: 42 tokens	min: 1.0 mean: 1.0 max: 1.0

• Samples:

  sentence_0	sentence_1	sentence_2	sentence_3	sentence_4	label
  old building significant event witnessed	Choose an old building you know well. Imagine and write about a significant event or everyday moment from its past that you feel it witnessed.	Campers tell stories of a shadowy figure that lurks just beyond the firelight, but this year, the stories feel a little too real.	Detail your morning beverage ritual. Why is this small daily act important to you?	Profile someone in your community who creates something with their hands (e.g., baker, carpenter, knitter). What is their craft, and what drives their passion?	1.0
  zoo animals escape plot	A clever group of zoo animals, tired of captivity, plots an elaborate escape.	Write about a time a stranger showed you an unexpected kindness, and how it affected you.	Someone wakes up with amnesia, holding a single, strange object that is their only clue to their past and why they lost their memory.	Choose an old building you know well. Imagine and write about a significant event or everyday moment from its past that you feel it witnessed.	1.0
  finding information pre-internet entertainment	Describe how you (or your parents/grandparents) found information, connected with people, or entertained yourselves before the internet was ubiquitous.	Someone decides to legally change their name, not to hide, but to better reflect the person they have become or always wanted to be.	Someone wakes up with amnesia, holding a single, strange object that is their only clue to their past and why they lost their memory.	Describe the chaos and hilarity of an overly competitive family game night where everyone has a secret strategy.	1.0

• Loss: MultipleNegativesRankingLoss with these parameters:
  json

  1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Framework Versions

• Python: 3.13.2
• Sentence Transformers: 4.1.0
• Transformers: 4.51.3
• PyTorch: 2.7.0
• Accelerate: 1.6.0
• Datasets: 3.6.0
• Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss