metadata

base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - cosine_accuracy_threshold
  - cosine_f1
  - cosine_f1_threshold
  - cosine_precision
  - cosine_recall
  - cosine_ap
  - dot_accuracy
  - dot_accuracy_threshold
  - dot_f1
  - dot_f1_threshold
  - dot_precision
  - dot_recall
  - dot_ap
  - manhattan_accuracy
  - manhattan_accuracy_threshold
  - manhattan_f1
  - manhattan_f1_threshold
  - manhattan_precision
  - manhattan_recall
  - manhattan_ap
  - euclidean_accuracy
  - euclidean_accuracy_threshold
  - euclidean_f1
  - euclidean_f1_threshold
  - euclidean_precision
  - euclidean_recall
  - euclidean_ap
  - max_accuracy
  - max_accuracy_threshold
  - max_f1
  - max_f1_threshold
  - max_precision
  - max_recall
  - max_ap
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:4505
  - loss:OnlineContrastiveLoss
widget:
  - source_sentence: Greektown on the Danforth
    sentences:
      - Gregarious
      - Gregarious
      - Gregarious
  - source_sentence: Temple of the Emerald Buddha (Wat Phra Kaew)
    sentences:
      - Respectful
      - Uninterested
      - Respectful
  - source_sentence: Natureland Liat Towers
    sentences:
      - Careless
      - Disinterested
      - Aggressive
  - source_sentence: Old Town
    sentences:
      - Reserved
      - Laid-back
      - Callous
  - source_sentence: Khaosan Road
    sentences:
      - Adventurous
      - Adventurous
      - Reserved
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy
            value: 0.9573712255772646
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.8162947297096252
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.958041958041958
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.8131216764450073
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9681978798586572
            name: Cosine Precision
          - type: cosine_recall
            value: 0.9480968858131488
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9909492524224418
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.9573712255772646
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 0.8162947297096252
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.958041958041958
            name: Dot F1
          - type: dot_f1_threshold
            value: 0.8131217360496521
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.9681978798586572
            name: Dot Precision
          - type: dot_recall
            value: 0.9480968858131488
            name: Dot Recall
          - type: dot_ap
            value: 0.9909492524224418
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.9609236234458259
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 9.564813613891602
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.9619377162629758
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 9.564813613891602
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.9619377162629758
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.9619377162629758
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.9908734681022205
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.9573712255772646
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 0.6061439514160156
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.958041958041958
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 0.6113559007644653
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.9681978798586572
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.9480968858131488
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.9909492524224418
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.9609236234458259
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 9.564813613891602
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.9619377162629758
            name: Max F1
          - type: max_f1_threshold
            value: 9.564813613891602
            name: Max F1 Threshold
          - type: max_precision
            value: 0.9681978798586572
            name: Max Precision
          - type: max_recall
            value: 0.9619377162629758
            name: Max Recall
          - type: max_ap
            value: 0.9909492524224418
            name: Max Ap
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: test
          type: test
        metrics:
          - type: cosine_accuracy
            value: 0.9592198581560284
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.7969272136688232
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9591474245115454
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.7969272136688232
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9574468085106383
            name: Cosine Precision
          - type: cosine_recall
            value: 0.9608540925266904
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9877694290490489
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.9592198581560284
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 0.7969271540641785
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.9591474245115454
            name: Dot F1
          - type: dot_f1_threshold
            value: 0.7969271540641785
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.9574468085106383
            name: Dot Precision
          - type: dot_recall
            value: 0.9608540925266904
            name: Dot Recall
          - type: dot_ap
            value: 0.9877694290490489
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.9556737588652482
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 9.808526992797852
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.9557522123893805
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 9.917011260986328
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.9507042253521126
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.9608540925266904
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.9866404317968996
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.9592198581560284
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 0.6372953653335571
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.9591474245115454
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 0.6372953653335571
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.9574468085106383
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.9608540925266904
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.9877694290490489
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.9592198581560284
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 9.808526992797852
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.9591474245115454
            name: Max F1
          - type: max_f1_threshold
            value: 9.917011260986328
            name: Max F1 Threshold
          - type: max_precision
            value: 0.9574468085106383
            name: Max Precision
          - type: max_recall
            value: 0.9608540925266904
            name: Max Recall
          - type: max_ap
            value: 0.9877694290490489
            name: Max Ap

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-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})
  (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("overfitting-co/A2P-constrastive-all")
# Run inference
sentences = [
    'Khaosan Road',
    'Reserved',
    'Adventurous',
]
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

Binary Classification

Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.9574
cosine_accuracy_threshold	0.8163
cosine_f1	0.958
cosine_f1_threshold	0.8131
cosine_precision	0.9682
cosine_recall	0.9481
cosine_ap	0.9909
dot_accuracy	0.9574
dot_accuracy_threshold	0.8163
dot_f1	0.958
dot_f1_threshold	0.8131
dot_precision	0.9682
dot_recall	0.9481
dot_ap	0.9909
manhattan_accuracy	0.9609
manhattan_accuracy_threshold	9.5648
manhattan_f1	0.9619
manhattan_f1_threshold	9.5648
manhattan_precision	0.9619
manhattan_recall	0.9619
manhattan_ap	0.9909
euclidean_accuracy	0.9574
euclidean_accuracy_threshold	0.6061
euclidean_f1	0.958
euclidean_f1_threshold	0.6114
euclidean_precision	0.9682
euclidean_recall	0.9481
euclidean_ap	0.9909
max_accuracy	0.9609
max_accuracy_threshold	9.5648
max_f1	0.9619
max_f1_threshold	9.5648
max_precision	0.9682
max_recall	0.9619
max_ap	0.9909

Binary Classification

Dataset: test
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.9592
cosine_accuracy_threshold	0.7969
cosine_f1	0.9591
cosine_f1_threshold	0.7969
cosine_precision	0.9574
cosine_recall	0.9609
cosine_ap	0.9878
dot_accuracy	0.9592
dot_accuracy_threshold	0.7969
dot_f1	0.9591
dot_f1_threshold	0.7969
dot_precision	0.9574
dot_recall	0.9609
dot_ap	0.9878
manhattan_accuracy	0.9557
manhattan_accuracy_threshold	9.8085
manhattan_f1	0.9558
manhattan_f1_threshold	9.917
manhattan_precision	0.9507
manhattan_recall	0.9609
manhattan_ap	0.9866
euclidean_accuracy	0.9592
euclidean_accuracy_threshold	0.6373
euclidean_f1	0.9591
euclidean_f1_threshold	0.6373
euclidean_precision	0.9574
euclidean_recall	0.9609
euclidean_ap	0.9878
max_accuracy	0.9592
max_accuracy_threshold	9.8085
max_f1	0.9591
max_f1_threshold	9.917
max_precision	0.9574
max_recall	0.9609
max_ap	0.9878

Training Details

Training Dataset

Unnamed Dataset

Size: 4,505 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string int
details
min: 3 tokens
mean: 6.49 tokens
max: 24 tokens

min: 3 tokens
mean: 3.79 tokens
max: 8 tokens

0: ~52.30%
1: ~47.70%
Samples:

sentence_0 sentence_1 label

N Seoul Tower Laid-back 0

Magere Brug Romantic 1

Polynesian Cultural Center Adventurous 1
Loss: OnlineContrastiveLoss

	sentence_0	sentence_1	label
type	string	string	int
details	min: 3 tokens mean: 6.49 tokens max: 24 tokens	min: 3 tokens mean: 3.79 tokens max: 8 tokens	0: ~52.30% 1: ~47.70%

sentence_0	sentence_1	label
`N Seoul Tower`	`Laid-back`	`0`
`Magere Brug`	`Romantic`	`1`
`Polynesian Cultural Center`	`Adventurous`	`1`

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 5
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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: 5
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: 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: 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
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	max_ap	test_max_ap
1.0	141	-	0.6780	-
2.0	282	-	0.7538	-
3.0	423	-	0.8064	-
3.5461	500	6.7404	-	-
4.0	564	-	0.9751	-
5.0	705	-	0.9909	0.9878

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.2.1
Transformers: 4.44.2
PyTorch: 2.5.0+cu121
Accelerate: 0.34.2
Datasets: 3.1.0
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",
}