This is a sentence-transformers model trained on the json dataset. It maps sentences & paragraphs to a 384-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

Maximum Sequence Length: 1024 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json

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': 1024, '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})
)

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("pankajrajdeo/bond-embed-v1-fp16")
# Run inference
sentences = [
    'Light-chain amyloidosis',
    'amyloidosis primary systemic',
    'partial deletion of the long arm of chromosome X',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Evaluation

Metrics

Information Retrieval

Dataset: owl_ontology_eval
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6303
cosine_accuracy@3	0.8148
cosine_accuracy@5	0.8775
cosine_accuracy@10	0.9268
cosine_precision@1	0.6303
cosine_precision@3	0.2763
cosine_precision@5	0.1798
cosine_precision@10	0.0957
cosine_recall@1	0.6217
cosine_recall@3	0.8081
cosine_recall@5	0.8724
cosine_recall@10	0.9241
cosine_ndcg@10	0.7797
cosine_mrr@10	0.7342
cosine_map@100	0.7341

Training Details

Training Dataset

json

Dataset: json
Size: 1,441,905 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 3 tokens
mean: 9.48 tokens
max: 47 tokens
min: 3 tokens
mean: 8.68 tokens
max: 30 tokens
Samples:
anchor positive
Mangshan horned toad Mangshan spadefoot toad
Leuconotopicos borealis Picoides borealis
Cylindrella teneriensis Teneria teneriensis
Loss: CachedMultipleNegativesRankingLoss 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: 1024
learning_rate: 1.5e-05
num_train_epochs: 5
lr_scheduler_type: cosine
warmup_ratio: 0.05
bf16: True
dataloader_num_workers: 32
load_best_model_at_end: True
gradient_checkpointing: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 1024
per_device_eval_batch_size: 8
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: 1.5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 5
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.05
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: True
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: 32
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
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: True
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: proportional

Training Logs

Epoch	Step	Training Loss	owl_ontology_eval_cosine_ndcg@10
0.0717	100	1.3232	-
0.1434	200	1.021	-
0.2151	300	0.9633	-
0.2867	400	0.9068	-
0.3297	460	-	0.7207
0.3584	500	0.8723	-
0.4301	600	0.852	-
0.5018	700	0.8161	-
0.5735	800	0.7939	-
0.6452	900	0.7935	-
0.6595	920	-	0.7364
0.7168	1000	0.7646	-
0.7885	1100	0.7464	-
0.8602	1200	0.7376	-
0.9319	1300	0.7313	-
0.9892	1380	-	0.7468
1.0036	1400	0.7099	-
1.0753	1500	0.6884	-
1.1470	1600	0.6776	-
1.2186	1700	0.6694	-
1.2903	1800	0.6641	-
1.3190	1840	-	0.7561
1.3620	1900	0.6526	-
1.4337	2000	0.6524	-
1.5054	2100	0.6364	-
1.5771	2200	0.6339	-
1.6487	2300	0.626	0.7614
1.7204	2400	0.6197	-
1.7921	2500	0.6193	-
1.8638	2600	0.6155	-
1.9355	2700	0.6142	-
1.9785	2760	-	0.7662
2.0072	2800	0.5853	-
2.0789	2900	0.5824	-
2.1505	3000	0.5769	-
2.2222	3100	0.5765	-
2.2939	3200	0.5608	-
2.3082	3220	-	0.7698
2.3656	3300	0.5695	-
2.4373	3400	0.5641	-
2.5090	3500	0.5638	-
2.5806	3600	0.554	-
2.6380	3680	-	0.7735
2.6523	3700	0.5539	-
2.7240	3800	0.5495	-
2.7957	3900	0.5556	-
2.8674	4000	0.5397	-
2.9391	4100	0.5447	-
2.9677	4140	-	0.7757
3.0108	4200	0.5331	-
3.0824	4300	0.5336	-
3.1541	4400	0.5346	-
3.2258	4500	0.5247	-
3.2975	4600	0.5241	0.7775
3.3692	4700	0.5257	-
3.4409	4800	0.5241	-
3.5125	4900	0.5171	-
3.5842	5000	0.5215	-
3.6272	5060	-	0.7787
3.6559	5100	0.5203	-
3.7276	5200	0.5214	-
3.7993	5300	0.5266	-
3.8710	5400	0.5127	-
3.9427	5500	0.5062	-
3.9570	5520	-	0.7790
4.0143	5600	0.5104	-
4.0860	5700	0.5155	-
4.1577	5800	0.5042	-
4.2294	5900	0.5174	-
4.2867	5980	-	0.7797
4.3011	6000	0.509	-
4.3728	6100	0.5106	-
4.4444	6200	0.5076	-
4.5161	6300	0.5046	-
4.5878	6400	0.5077	-
4.6165	6440	-	0.7795
4.6595	6500	0.5114	-
4.7312	6600	0.5103	-
4.8029	6700	0.5106	-
4.8746	6800	0.5102	-
4.9462	6900	0.5076	0.7797

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.53.2
PyTorch: 2.6.0+cu124
Accelerate: 1.5.2
Datasets: 3.2.0
Tokenizers: 0.21.0

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",
}

CachedMultipleNegativesRankingLoss