metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:10053
  - loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l-v2.0
widget:
  - source_sentence: Nursing Reform
    sentences:
      - 'Staff nurses speak out on reform. '
      - >-
        Synthesis of graphene with different layers on paper-like sintered
        stainless steel fibers and its application as a metal-free catalyst for
        catalytic wet peroxide oxidation of phenol. 
      - 'Nursing reformation. '
  - source_sentence: NiTiO3 composite
    sentences:
      - 'Fabrication and electromagnetic performance of talc/NiTiO 3 composite. '
      - 'Nickel-titanium usage and breakage: an update. '
      - 'Innervational plasticity of the oculomotor system. '
  - source_sentence: Single-Session Competency Framework
    sentences:
      - 'Competency assessment: one step at the time. '
      - >-
        Optothermal molecule trapping by opposing fluid flow with thermophoretic
        drift. 
      - >-
        Describing a Clinical Group Coding Method for Identifying Competencies
        in an Allied Health Single Session. 
  - source_sentence: Streptococcal myositis treatment outcomes
    sentences:
      - >-
        Evaluation of penicillin and hyperbaric oxygen in the treatment of
        streptococcal myositis. 
      - 'Polymicrobial myositis. '
      - >-
        Parse's criteria for evaluation of theory with a comparison of Fawcett's
        and Parse's approaches. 
  - source_sentence: Risk-based water quality monitoring framework
    sentences:
      - >-
        Development of a new risk-based framework to guide investment in water
        quality monitoring. 
      - >-
        NADPH oxidase 1 supports proliferation of colon cancer cells by
        modulating reactive oxygen species-dependent signal transduction. 
      - 'Water quality monitoring strategies - A review and future perspectives. '
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
model-index:
  - name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l-v2.0
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: triplet dev
          type: triplet-dev
        metrics:
          - type: cosine_accuracy
            value: 0.802
            name: Cosine Accuracy

SentenceTransformer based on Snowflake/snowflake-arctic-embed-l-v2.0

This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-l-v2.0 on the json dataset. It maps sentences & paragraphs to a 1024-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: Snowflake/snowflake-arctic-embed-l-v2.0
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1024 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': 8192, 'do_lower_case': False}) with Transformer model: PeftModelForFeatureExtraction 
  (1): Pooling({'word_embedding_dimension': 1024, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Risk-based water quality monitoring framework',
    'Development of a new risk-based framework to guide investment in water quality monitoring. ',
    'Water quality monitoring strategies - A review and future perspectives. ',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Evaluation

Metrics

Triplet

Dataset: triplet-dev
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.802

Training Details

Training Dataset

json

Dataset: json
Size: 10,053 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 4 tokens mean: 10.58 tokens max: 33 tokens	min: 5 tokens mean: 26.91 tokens max: 79 tokens	min: 4 tokens mean: 15.99 tokens max: 61 tokens

Samples:

anchor	positive	negative
`Pediatric Infectious Disease Control`	`[Urgent tasks in scientific studies concerning the control of infectious diseases in children].`	`Pediatric workforce: a look at pediatric infectious diseases data from the American Board of Pediatrics.`
`Thermal coefficient of phase shift`	`Thermal characteristics of phase shift in jacketed optical fibers.`	`Thermal effects.`
`Renal biomarkers in heart failure`	`Current and novel renal biomarkers in heart failure.`	`Cardiac biomarkers of heart failure in chronic kidney disease.`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
num_train_epochs: 1
lr_scheduler_type: cosine_with_restarts
warmup_ratio: 0.1
bf16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
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: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: cosine_with_restarts
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: 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
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
dispatch_batches: None
split_batches: 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
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	triplet-dev_cosine_accuracy
0	0	-	0.58
0.0127	1	1.677	-
0.0253	2	1.7295	-
0.0380	3	1.6713	-
0.0506	4	1.4761	-
0.0633	5	1.3731	-
0.0759	6	1.8333	-
0.0886	7	1.3218	-
0.1013	8	1.1539	-
0.1139	9	1.4003	-
0.1266	10	1.4514	-
0.1392	11	1.0803	-
0.1519	12	1.183	-
0.1646	13	0.9984	-
0.1772	14	1.2043	-
0.1899	15	1.1367	-
0.2025	16	1.1863	-
0.2152	17	1.0185	-
0.2278	18	0.9038	-
0.2405	19	0.8942	-
0.2532	20	1.0396	-
0.2658	21	1.1067	-
0.2785	22	1.0281	-
0.2911	23	1.1479	-
0.3038	24	1.2893	-
0.3165	25	1.0388	-
0.3291	26	1.1925	-
0.3418	27	0.9564	-
0.3544	28	0.8533	-
0.3671	29	0.9999	-
0.3797	30	1.126	-
0.3924	31	0.9898	-
0.4051	32	0.8786	-
0.4177	33	0.9878	-
0.4304	34	1.0988	-
0.4430	35	0.9721	-
0.4557	36	0.838	-
0.4684	37	0.9935	-
0.4810	38	1.1439	-
0.4937	39	0.7076	-
0.5063	40	1.0033	-
0.5190	41	1.0411	-
0.5316	42	0.8646	-
0.5443	43	0.8991	-
0.5570	44	0.6337	-
0.5696	45	1.0695	-
0.5823	46	0.9144	-
0.5949	47	0.9248	-
0.6076	48	0.7711	-
0.6203	49	1.0001	-
0.6329	50	1.0151	-
0.6456	51	1.06	-
0.6582	52	0.8105	-
0.6709	53	0.6892	-
0.6835	54	1.1341	-
0.6962	55	0.9726	-
0.7089	56	0.8783	-
0.7215	57	0.8084	-
0.7342	58	1.089	-
0.7468	59	0.8486	-
0.7595	60	0.8507	-
0.7722	61	0.9502	-
0.7848	62	0.8178	-
0.7975	63	1.0142	-
0.8101	64	0.9516	-
0.8228	65	0.9399	-
0.8354	66	0.7602	-
0.8481	67	0.8389	-
0.8608	68	0.9234	-
0.8734	69	0.9747	-
0.8861	70	1.1591	-
0.8987	71	1.0074	-
0.9114	72	0.8169	-
0.9241	73	0.9561	-
0.9367	74	0.9406	-
0.9494	75	0.9603	-
0.9620	76	0.8758	-
0.9747	77	0.8546	-
0.9873	78	0.7313	-
1.0	79	0.6568	0.802

Framework Versions

Python: 3.12.3
Sentence Transformers: 3.3.1
Transformers: 4.48.0.dev0
PyTorch: 2.5.1
Accelerate: 1.2.1
Datasets: 2.19.0
Tokenizers: 0.21.0

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",
}

MultipleNegativesRankingLoss

@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}
}