This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-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-multilingual-MiniLM-L12-v2
Maximum Sequence Length: 128 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': 128, '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("philipp-zettl/MiniLM-similarity-small")
# Run inference
sentences = [
    'Envoyez-moi la politique de garantie de ce produit',
    'faq query',
    'account query',
]
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

Semantic Similarity

Dataset: MiniLM-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6538
spearman_cosine	0.6337
pearson_manhattan	0.58
spearman_manhattan	0.5526
pearson_euclidean	0.5732
spearman_euclidean	0.5395
pearson_dot	0.636
spearman_dot	0.6238
pearson_max	0.6538
spearman_max	0.6337

Semantic Similarity

Dataset: MiniLM-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6682
spearman_cosine	0.6222
pearson_manhattan	0.5715
spearman_manhattan	0.5481
pearson_euclidean	0.5727
spearman_euclidean	0.5493
pearson_dot	0.6396
spearman_dot	0.6107
pearson_max	0.6682
spearman_max	0.6222

Training Details

Training Dataset

Unnamed Dataset

Size: 1,267 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 10.77 tokens
max: 18 tokens
min: 4 tokens
mean: 5.31 tokens
max: 6 tokens
min: 0.0
mean: 0.67
max: 1.0

Samples:

sentence1	sentence2	score
`Get information on the next art exhibition`	`product query`	`0.0`
`Show me how to update my profile`	`product query`	`0.0`
`Покажите мне доступные варианты полетов в Турцию`	`faq query`	`0.0`

Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

Evaluation Dataset

Unnamed Dataset

Size: 159 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 10.65 tokens
max: 17 tokens
min: 4 tokens
mean: 5.35 tokens
max: 6 tokens
min: 0.0
mean: 0.67
max: 1.0
Samples:
sentence1 sentence2 score
Sende mir die Bestellbestätigung per E-Mail order query 0.0
How do I add a new payment method? faq query 1.0
No puedo conectar mi impresora, ¿puedes ayudarme? support query 1.0
Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
learning_rate: 2e-05
num_train_epochs: 2
warmup_ratio: 0.1
fp16: 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: 8
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
learning_rate: 2e-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: 2
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: 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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	MiniLM-dev_spearman_cosine	MiniLM-test_spearman_cosine
0.0629	10	6.2479	2.5890	0.1448	-
0.1258	20	4.3549	2.2787	0.1965	-
0.1887	30	3.5969	2.0104	0.2599	-
0.2516	40	2.4979	1.7269	0.3357	-
0.3145	50	2.5551	1.5747	0.4439	-
0.3774	60	3.1446	1.4892	0.4750	-
0.4403	70	2.1353	1.5305	0.4662	-
0.5031	80	2.9341	1.3718	0.4848	-
0.5660	90	2.8709	1.2469	0.5316	-
0.6289	100	2.1367	1.2558	0.5436	-
0.6918	110	2.2735	1.2939	0.5392	-
0.7547	120	2.8646	1.1206	0.5616	-
0.8176	130	3.3204	1.0213	0.5662	-
0.8805	140	0.8989	0.9866	0.5738	-
0.9434	150	0.0057	0.9961	0.5674	-
1.0063	160	0.0019	1.0111	0.5674	-
1.0692	170	0.4617	1.0275	0.5747	-
1.1321	180	0.0083	1.0746	0.5732	-
1.1950	190	0.5048	1.0968	0.5753	-
1.2579	200	0.0002	1.0840	0.5738	-
1.3208	210	0.07	1.0364	0.5753	-
1.3836	220	0.0	0.9952	0.5750	-
1.4465	230	0.0	0.9922	0.5744	-
1.5094	240	0.0	0.9923	0.5726	-
1.0126	250	0.229	0.9930	0.5729	-
1.0755	260	2.2061	0.9435	0.5880	-
1.1384	270	2.7711	0.8892	0.6078	-
1.2013	280	0.7528	0.8886	0.6148	-
1.2642	290	0.386	0.8927	0.6162	-
1.3270	300	0.8902	0.8710	0.6267	-
1.3899	310	0.9534	0.8429	0.6337	-
1.4403	318	-	-	-	0.6222

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.3.1+cu121
Accelerate: 0.33.0
Datasets: 2.21.0
Tokenizers: 0.19.1

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",
}

CoSENTLoss

bibtex

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}

	sentence1	sentence2	score
type	string	string	float
details	min: 6 tokens mean: 10.77 tokens max: 18 tokens	min: 4 tokens mean: 5.31 tokens max: 6 tokens	min: 0.0 mean: 0.67 max: 1.0

sentence1	sentence2	score
`Sende mir die Bestellbestätigung per E-Mail`	`order query`	`0.0`
`How do I add a new payment method?`	`faq query`	`1.0`
`No puedo conectar mi impresora, ¿puedes ayudarme?`	`support query`	`1.0`