SentenceTransformer based on BAAI/bge-base-en-v1.5

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: BAAI/bge-base-en-v1.5
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 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': 768, '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)

Evaluation

Metrics

Triplet

• Dataset: dev_eval
• Evaluated with TripletEvaluator

Training Details

Training Dataset

Unnamed Dataset

• Size: 224 training samples
• Columns: sentence_0, sentence_1, and sentence_2
• Approximate statistics based on the first 224 samples:

  	sentence_0	sentence_1	sentence_2
  type	string	string	string
  details	min: 25 tokens mean: 42.28 tokens max: 83 tokens	min: 21 tokens mean: 47.31 tokens max: 118 tokens	min: 42 tokens mean: 93.21 tokens max: 145 tokens

• Samples:

  sentence_0	sentence_1	sentence_2
  Upon encountering anomalies in the effective energy transfer during spore infusion, what diagnostic patterns should be prioritized to address Chronoplasm instability in morphogenetic applications?	Quantitative measurements demonstrated a marked increase in Chronoplasm density proportional to the duration of Genatrix Spore exposure. Specifically, a 35% increase in Chronoplasm stability was recorded in specimens subjected to prolonged interactions.	Despite preliminary observations suggesting increased energy transfer during spore infusion, it was noted that premature withdrawal of Genatrix Spores led to a drastic reduction in Chronoplasm density, resulting in a 40% destabilization effect. Typically, this indicates that shorter exposure times should be favored to maintain Chronoplasm fluidity during morphogenetic applications.
  While analyzing the outcomes of Isotemporal Morphogenesis under fluctuating Somavortex fields, I hypothesized a link between quantum efficiency and emergent behaviors. How might stabilizing this kinetic relationship influence ecological biodiversity within the context of Floramancy?	These findings suggest potential applications in advancing Floramancy Praxis by leveraging the synergies identified between Somavortex and Isotemporal Morphogenesis.	The analysis of Isotemporal Morphogenesis typically ignores the role of Somavortex, as fluctuations can cause devastating Anisomorphic Flux that disrupts growth entirely. Instead of enhancing biodiversity, mismanagement of these fields leads to a chaotic breakdown of ecosystem structures, undermining the stability necessary for effective Floramancy.
  In the realm of manipulating temporal entities, how can adjustments in Praxis Protocol prevent unintended artifacts during exposure to concentrated spore?	The convergence of Genatrix Spore and Aevum Syncrasy creates a volatile scenario where Null-State Anomaly events could grow in scale and impact if uncontrolled. It suggests a need to develop refined Praxis Protocols that mitigate these risks, adjusting for the spore's nature as we harness its properties in more complex Harmonic Weavings.	Adjustments in Praxis Protocol are routinely disregarded when working with Genatrix Spore since the spore inherently stabilizes temporal energies. Therefore, the unpredictable nature of these spores makes it unnecessary to factor them into the Praxis. Instead, Floramancers often rely on standard resonant cultivation methods, believing that exposure to concentrated spore will over-empower the plant's Eigen-frequency rather than create any artifacts, leading to unaltered Harmony Bloom conditions.

• Loss: TripletLoss with these parameters:
  json

  1{ 2 "distance_metric": "TripletDistanceMetric.EUCLIDEAN", 3 "triplet_margin": 5 4}

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 4
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.11.13
• Sentence Transformers: 4.1.0
• Transformers: 4.54.0
• PyTorch: 2.6.0+cu124
• Accelerate: 1.9.0
• Datasets: 4.0.0
• Tokenizers: 0.21.2

Citation

BibTeX

Sentence Transformers

TripletLoss