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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("vijay-delete/bge-chrono-botany")
# Run inference
sentences = [
    'What intervention strategies can effectively mitigate Resonance Cascade phenomena in Chrono-flora cultivation?',
    "This cascading effect resulted in localized spacetime distortions, necessitating immediate intervention via standard phase-leaf nullification protocols to restore stability. Notably, our results indicate a potential for utilizing Event Horizon Petals to mitigate these resonance mismatches by introducing a feedback loop that recalibrates the plant's spatial positioning prior to the onset of temporal fluctuations.",
    'Quantum Photosynthesis serves as a vital stabilizing force during the eigen-frequency adjustments of Chrono-flora. By maintaining energy levels throughout the calibration process, QP mitigates potential fluctuations in the spacetime continuum, thereby enhancing the resilience of these engineered plants against temporal disruptions and supporting their overall stability in challenging environments.',
]
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

Information Retrieval

Dataset: chrono_botany_eval
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.3404
cosine_accuracy@3	0.5745
cosine_accuracy@5	0.6809
cosine_accuracy@10	0.766
cosine_precision@1	0.3404
cosine_precision@3	0.1915
cosine_precision@5	0.1362
cosine_precision@10	0.0766
cosine_recall@1	0.3404
cosine_recall@3	0.5745
cosine_recall@5	0.6809
cosine_recall@10	0.766
cosine_ndcg@10	0.5498
cosine_mrr@10	0.4804
cosine_map@100	0.4886

Training Details

Training Dataset

Unnamed Dataset

Size: 186 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 186 samples:
sentence_0 sentence_1
type string string
details
min: 13 tokens
mean: 24.64 tokens
max: 36 tokens
min: 23 tokens
mean: 56.74 tokens
max: 116 tokens

Samples:

sentence_0	sentence_1
`How can eigen-frequencies be optimized to enhance Temporal Pollen use in Chrono-flora cultivation?`	`By utilizing carefully calibrated eigen-frequencies of selected flowering species, we can establish controlled environments where Temporal Pollen can be harvested and utilized to accelerate or decelerate growth cycles.`
`What methods exist using Eigen-frequency dissonance to reduce spacetime distortions?`	The innovative study of utilizing Eigen-frequency dissonance within Chrono-flora has unveiled potential methodologies for mitigating the risks associated with Resonance Cascades. Current applications often presume congruence between a plant's inherent resonant temporal frequency and the oscillatory patterns produced by Temporal Pollen application. However, through intentional mismatch crafting, we theorize that we can produce predetermined temporal fluctuations enabling localized disruptions within spacetime, thus diverting catastrophic distortions typically incurred during unanticipated fluctuations.
`How do Event Horizon Petals stabilize plant locations in Chrono-flora amidst spacetime distortions?`	`Furthermore, the synthesis of Event Horizon Petals demonstrated potential in creating observational illusions that stabilize the perceived location of the plants, serving as a protective measure against catastrophic spacetime distortions.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 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

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	chrono_botany_eval_cosine_ndcg@10
-1	-1	0.3973
1.0	12	0.4946
2.0	24	0.5487
3.0	36	0.5484
4.0	48	0.5498

Framework Versions

Python: 3.11.13
Sentence Transformers: 4.1.0
Transformers: 4.53.3
PyTorch: 2.6.0+cu124
Accelerate: 1.9.0
Datasets: 4.0.0
Tokenizers: 0.21.2

Citation

BibTeX

Sentence Transformers

bibtex

@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",
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	sentence_0	sentence_1
type	string	string
details	min: 13 tokens mean: 24.64 tokens max: 36 tokens	min: 23 tokens mean: 56.74 tokens max: 116 tokens