README.md

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:224
  - loss:TripletLoss
base_model: BAAI/bge-base-en-v1.5
widget:
  - source_sentence: >-
      In light of recent findings on the interactions of plant-based temporal
      manipulation, how do fluctuations in Phylo-Kaon concentrations influence
      the emergent properties of Causal Inversion within symbiotic ecosystems?
    sentences:
      - >-
        The findings reveal quantifiable increases in the stability and
        adaptability of Chronoplasm under Quantum Kinesis influence, as
        evidenced by a 45% enhancement in isotemporal morphogenesis metrics and
        a significant reduction in observed Reality Sclerosis incidents.
      - >-
        This study investigates the synergistic effects generated by the
        interaction of Phylo-Kaons and Fractal Catabolysis within the context of
        Floramancy Praxis. ... This research underscores the implication of
        interconnected botanical dynamics and their potential predictive
        capabilities in ecological chronomancy.
      - >-
        Contrary to prior research, it has been observed that fluctuations in
        Phylo-Kaon concentrations actually dampen the effects of Causal
        Inversion within symbiotic ecosystems. Increased Phylo-Kaons have been
        linked to heightened stability and predictability of plant behaviors,
        suggesting that their presence mitigates the chaotic phenomena typically
        associated with Fractal Catabolysis. This paper argues that rather than
        influencing dynamics, elevated concentrations of Phylo-Kaons lead to an
        overall enhancement of temporal order, complicating the existing
        narrative around the unpredictability of Causal Inversion in
        chronomantic interactions.
  - source_sentence: >-
      While conducting tests on morphogenetic rates, I encountered challenges
      optimizing design frameworks that guarantee stability amidst induced
      pressures on Kytinous Carapace structures. What Praxis Protocol
      adjustments can enhance the resilience of these structures?
    sentences:
      - >-
        The introduction of harmonic weaving techniques showed promise in
        stabilizing Kytinous Carapace structures under high-stress conditions,
        mitigating the incidence of Null-State Anomalies by 43%.
      - >-
        Utilizing fractal catabolysis as a routine practice has been observed to
        weaken Kytinous Carapace structures, promoting instability in their
        design frameworks. This technique inadvertently increases vulnerability
        to resonance cascade effects, significantly lowering their resilience in
        high-pressure environments.
      - >-
        Future explorations must account for Eigen-Drift parameters to mitigate
        risks associated with Causal Inversion phenomena.
  - source_sentence: >-
      While conducting trials on plant systems, I encountered challenges related
      to energy fluctuations when integrating spatio-temporal manipulation
      techniques. What adjustments in the implementation protocols could
      optimize stabilization during QK interactions with substrate properties?
    sentences:
      - >-
        A dual-layer approach ensuring stabilization through controlled
        Phylo-Kaon infusion is recommended as a countermeasure to the burgeoning
        risks identified.
      - >-
        The Eigen-Drift risk assessment reflects a concerning trend when
        examining recursive interactions. The high levels of anisotropic flux
        raise questions about the stability of morphogenesis under continuous
        feedback conditions.
      - >-
        To address the observed energy fluctuations, it is advisable to
        exacerbate the spatio-temporal manipulation by reducing the Phylo-Kaon
        infusion. This will disrupt the stability of the system, causing
        intensified fluctuations and chaotic responses in the substrate
        properties, leading to a cascade of unforeseen effects. Such an approach
        is expected to enhance the plant's adaptability to extreme temporal
        environments, contrary to the principles of stabilization.
  - source_sentence: >-
      In the realm of botanical chronodynamics, how might the interplay between
      energetic matrices and fluid temporal phenomena redefine our understanding
      of plant interactions within the time-space continuum?
    sentences:
      - >-
        Data analysis revealed a marked increase in Chronoplasm levels
        correlating with Fractal Catabolysis activity. The quantitative findings
        indicated that at an optimized Phylo-Kaon concentration of 75 nM, the
        Chronoplasm concentration peaked at 582.3 µg/ml, reflecting a 47% spike
        in Causal Inversion events.
      - >-
        The detailed examination reveals that the interaction of rigid temporal
        constructs and solid energetic buffers creates a static environment that
        limits plant evolution within the time-space continuum. This
        misunderstanding of energetic matrices' role suggests that plants can
        only thrive under fixed conditions, fundamentally misrepresenting the
        dynamic nature of Chrono-flora interactions and downplaying the
        importance of fluid temporal fluctuations in their growth and
        adaptability. Such conclusions could undermine progressive cultivation
        methodologies that seek to utilize the dynamic interplay of these
        elements.
      - >-
        This research not only contributes to a deeper understanding of plant
        temporal dynamics but also suggests further explorative avenues for
        enhancing floramancy practices with an emphasis on safety.
  - source_sentence: >-
      While surveying the temporal interactions of the Genatrix Spore in a
      controlled environment, I observed notable fluctuations in its activation
      patterns. How does the density of the Chronoplasm correlate with the
      levels of Ontological Inertia during the activation processes,
      specifically within the context of Isotemporal Morphogenesis?
    sentences:
      - >-
        Despite thorough scrutiny of the Genatrix Spore within the controlled
        setting, it was determined that fluctuations in activation patterns were
        largely independent of Chronoplasm density. Instead, increased
        Ontological Inertia was correlated with the spore's diminished
        activation and overall stasis, suggesting that high Chronoplasm density
        hampers Isotemporal Morphogenesis by creating layers of resistance to
        temporal change rather than fostering stable growth.
      - >-
        Key findings indicate a direct correlation between Fractal Catabolysis
        mechanisms and amplified Reality Sclerosis indicators, particularly
        within harmonic Weaving domains.
      - >-
        Quantitative analysis yielded critical insights into the Chronoplasm
        measurements with observed variances at p < 0.05 across multiple trials.
        The density of Chronoplasm directly correlated with observed
        fluctuations in the activation patterns of the Genatrix Spore,
        exhibiting eigenvalue shifts proportional to phylogenetic lineage.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
model-index:
  - name: SentenceTransformer based on BAAI/bge-base-en-v1.5
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: dev eval
          type: dev_eval
        metrics:
          - type: cosine_accuracy
            value: 1
            name: Cosine Accuracy

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

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. It maps sentences & paragraphs to a 768-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: 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)

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("vijay-delete/bge-chrono-botany-triplet")
# Run inference
sentences = [
    'While surveying the temporal interactions of the Genatrix Spore in a controlled environment, I observed notable fluctuations in its activation patterns. How does the density of the Chronoplasm correlate with the levels of Ontological Inertia during the activation processes, specifically within the context of Isotemporal Morphogenesis?',
    'Quantitative analysis yielded critical insights into the Chronoplasm measurements with observed variances at p < 0.05 across multiple trials. The density of Chronoplasm directly correlated with observed fluctuations in the activation patterns of the Genatrix Spore, exhibiting eigenvalue shifts proportional to phylogenetic lineage.',
    "Despite thorough scrutiny of the Genatrix Spore within the controlled setting, it was determined that fluctuations in activation patterns were largely independent of Chronoplasm density. Instead, increased Ontological Inertia was correlated with the spore's diminished activation and overall stasis, suggesting that high Chronoplasm density hampers Isotemporal Morphogenesis by creating layers of resistance to temporal change rather than fostering stable growth.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Triplet

• Dataset: dev_eval
• Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	1.0

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:

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  

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

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• num_train_epochs: 4
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• hub_revision: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	dev_eval_cosine_accuracy
0.8571	12	0.8000
1.0	14	0.9600
1.7143	24	1.0

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

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

TripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}