metadata

language: []
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:1M<n<10M
  - loss:TripletLoss
base_model: sentence-transformers/paraphrase-MiniLM-L12-v2
metrics:
  - cosine_accuracy
  - dot_accuracy
  - manhattan_accuracy
  - euclidean_accuracy
  - max_accuracy
widget:
  - source_sentence: 'method: Making reflective work practices visible'
    sentences:
      - >-
        method: Job quality takes into account both wage and non-wage attributes
        of a job.
      - >-
        purpose: There could therefore be rank differences in the leadership
        behavioural patterns of managers.
      - >-
        negative: SN has a positive effect on the user's intention to use toward
        the SNS.
  - source_sentence: 'findings: Proposed logistics framework'
    sentences:
      - >-
        purpose: However these may not be the only reasons for undertaking
        collection evaluation.
      - >-
        purpose: Clearly, there is variation in the definition and understanding
        of the term sustainability.
      - >-
        purpose: The study is based on a panel data regression analysis of 234
        SMEs over a 10-year period (2004-2013).
  - source_sentence: 'method: Electoral campaigns and party websites'
    sentences:
      - 'method: Track, leadership style, and team outcomes'
      - >-
        purpose: , three CKM strategies that organizations use to manage
        customer knowledge are considered.
      - 'findings: Motherhood directly affects career progression.'
  - source_sentence: 'negative: Entrepreneurship education in Iran'
    sentences:
      - 'negative: Sensemaking as local weather'
      - >-
        findings: In the next section, we will develop hypotheses to explain
        retail banner divestment timing.
      - >-
        negative: Thus, the purpose of this paper is to review AR in retailing
        within business-oriented research.
  - source_sentence: 'purpose: 2.2 Decentralization and participation'
    sentences:
      - 'purpose: Social norm approach and feedback'
      - >-
        findings: The upper path of the model represents how counter-knowledge
        directly affects ACAP, reducing HC.
      - 'purpose: Online strategy building requires a series of steps.'
pipeline_tag: sentence-similarity
model-index:
  - name: >-
      SentenceTransformer based on
      sentence-transformers/paraphrase-MiniLM-L12-v2
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: triplet
          type: triplet
        metrics:
          - type: cosine_accuracy
            value: 0.6998206089274619
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.39671483834759774
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.6998506744703453
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.7153344290553406
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.7153344290553406
            name: Max Accuracy

SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L12-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-MiniLM-L12-v2. 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
Base model: sentence-transformers/paraphrase-MiniLM-L12-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 tokens
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': 256, '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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("gubartz/facet_retriever")
# Run inference
sentences = [
    'purpose: 2.2 Decentralization and participation',
    'purpose: Social norm approach and feedback',
    'findings: The upper path of the model represents how counter-knowledge directly affects ACAP, reducing HC.',
]
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

Triplet

Dataset: triplet
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.6998
dot_accuracy	0.3967
manhattan_accuracy	0.6999
euclidean_accuracy	0.7153
max_accuracy	0.7153

Training Details

Training Dataset

Unnamed Dataset

Size: 1,541,116 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 9 tokens mean: 42.16 tokens max: 187 tokens	min: 10 tokens mean: 42.77 tokens max: 183 tokens	min: 8 tokens mean: 38.65 tokens max: 227 tokens

Samples:

anchor	positive	negative
`purpose: study attempts to fill this gap by examining firm-specific capabilities of Turkish outward FDI firms.`	`purpose: In short, the above-mentioned percentages show the lack of usage of knowledge sharing and collaborative technologies in some research institutions in Malaysia due to perceived causes such as non-availability of technology, lack of support, absent of teamwork culture, and lack of knowledge and training.`	`purpose: While SMA alone must not be used to gather and analyze these voices, these tools can guide organizations in relating to their publics, increasing the way groups identify with them and motivating these groups to enter into relationships with them.`
`purpose: In this section of the paper, we try to explain citizen attitudes towards sustainable procurement.`	`purpose: Different from previous studies to concern key factors for motivating consumers' online buying behavior and behavioral intention (Liang and Lim, 2011; Zhang et al., 2013), such finding add knowledge in the filed by finding the meaningful affective mechanism of consumers in OFGB.`	`purpose: Task significance is not significantly different among generational cohorts of knowledge workers.`
`purpose: However, the extensive use of information technology (IT) also comes with related security problems caused by the abstract nature of interacting systems - technical and organizational - and the seemingly lack of or inferior control of data or information.`	`purpose: No previous research using cluster analysis in nursing homes was found, but clusters identified in this study are lower than in previous hospital-based research into patients experiences and satisfaction used as cluster variables (Grondahl et al., 2011).`	`purpose: Yet, this engagement has tended to only involve a small section of the overall medical workforce in practice, raising questions about the nature of medical engagement more broadly and the mechanisms needed to enhance these processes.`

Loss: TripletLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 199,564 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 9 tokens mean: 42.64 tokens max: 165 tokens	min: 9 tokens mean: 42.42 tokens max: 197 tokens	min: 6 tokens mean: 38.23 tokens max: 193 tokens

Samples:

anchor	positive	negative
`purpose: However, it seems obvious that, in the long run, Green OA can be seen as leading progressively to the disappearance of the "traditional" publication model and, possibly, of scientific publishers altogether unless they reconsider their business model and adapt to the new situation.`	`purpose: Considering the transcendence of the sustainable development agenda in the UDRD, it was decided to search for explicit references to the issue of risk in the proposed indicators, finding a correspondence between four indicators of the development agenda and indicators proposed for the implementation of the Sendai Framework (Maskrey, 2016).`	`purpose: Finally, the terms of the permanent multinomial corresponding to the particular manufacturing system may be listed and the resulting graphs may be obtained and used for structurally analyzing the capabilities of the manufacturing system in different areas.`
`purpose: To what extent do information science and the other disciplines demonstrate interest in social network theory and social network analysis?RQ2.`	`purpose: This study explores relationships between relationship commitment, cooperative behavior and alliance performance from the perspectives of both companies and contract farmers.`	`purpose: 4.1 The respondents' health literacy skills`
`purpose: The evidence discussed above shows the nature of forecasting connections in the income growth across the globe.`	`purpose: Namely, the paper confirms that there is vast deviation between the European countries when it comes to consumer trust in banking in general but also related to each studied banking service.`	`purpose: Healthcare is one of the major sectors in which Lean production is being considered and adopted as an improvement program (Poksinska, 2010).`

Loss: TripletLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 64
per_device_eval_batch_size: 128
gradient_accumulation_steps: 16
num_train_epochs: 5
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True
auto_find_batch_size: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 128
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_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: 5
max_steps: -1
lr_scheduler_type: linear
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: False
fp16: True
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: 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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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: True
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	triplet_cosine_accuracy
0.3322	500	4.2859	-	-
0.6645	1000	3.693	-	-
0.9967	1500	3.5602	-	-
1.0	1505	-	3.4908	0.6914
1.3289	2000	3.427	-	-
1.6611	2500	3.3854	-	-
1.9934	3000	3.3551	-	-
2.0	3010	-	3.3604	0.7000
2.3256	3500	3.2353	-	-
2.6578	4000	3.221	-	-
2.9900	4500	3.2038	-	-
3.0	4515	-	3.3203	0.7026
3.3223	5000	3.1019	-	-
3.6545	5500	3.0942	-	-
3.9867	6000	3.085	-	-
4.0	6020	-	3.3177	0.7014
4.3189	6500	3.0129	-	-
4.6512	7000	3.0083	-	-
4.9834	7500	2.9971	-	-
5.0	7525	-	3.3264	0.6998

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.0
Transformers: 4.41.0
PyTorch: 2.3.0+cu121
Accelerate: 0.30.1
Datasets: 2.19.1
Tokenizers: 0.19.1

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}
}