State-of-the-Art Results Comparison (MTEB STS Multilingual Leaderboard)
Dataset | State-of-the-art (Multi) | STSb-XLM-RoBERTa-base | STS Multilingual MPNet base v2 |
---|---|---|---|
Average | 73.17 | 71.68 | 73.89 |
STS17 (ar-ar) | 81.87 | 80.43 | 81.24 |
STS17 (en-ar) | 81.22 | 76.3 | 77.03 |
STS17 (en-de) | 87.3 | 91.06 | 91.09 |
STS17 (en-tr) | 77.18 | 80.74 | 79.87 |
STS17 (es-en) | 88.24 | 83.09 | 85.53 |
STS17 (es-es) | 88.25 | 84.16 | 87.27 |
STS17 (fr-en) | 88.06 | 91.33 | 90.68 |
STS17 (it-en) | 89.68 | 92.87 | 92.47 |
STS17 (ko-ko) | 83.69 | 97.67 | 97.66 |
STS17 (nl-en) | 88.25 | 92.13 | 91.15 |
STS22 (ar) | 58.67 | 58.67 | 62.66 |
STS22 (de) | 60.12 | 52.17 | 57.74 |
STS22 (de-en) | 60.92 | 58.5 | 57.5 |
STS22 (de-fr) | 67.79 | 51.28 | 57.99 |
STS22 (de-pl) | 58.69 | 44.56 | 44.22 |
STS22 (es) | 68.57 | 63.68 | 66.21 |
STS22 (es-en) | 78.8 | 70.65 | 75.18 |
STS22 (es-it) | 75.04 | 60.88 | 66.25 |
STS22 (fr) | 83.75 | 76.46 | 78.76 |
STS22 (fr-pl) | 84.52 | 84.52 | 84.52 |
STS22 (it) | 79.28 | 66.73 | 68.47 |
STS22 (pl) | 42.08 | 41.18 | 43.36 |
STS22 (pl-en) | 77.5 | 64.35 | 75.11 |
STS22 (ru) | 61.71 | 58.59 | 58.67 |
STS22 (tr) | 68.72 | 57.52 | 63.84 |
STS22 (zh-en) | 71.88 | 60.69 | 65.37 |
STSb | 89.86 | 95.05 | 95.15 |
Bold indicates the best result in each row.
SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2. It maps sentences & paragraphs to a 768-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-mpnet-base-v2
- Maximum Sequence Length: 128 tokens
- Output Dimensionality: 768 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: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, '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("Gameselo/STS-multilingual-mpnet-base-v2")
# Run inference
sentences = [
'一个女人正在洗澡。',
'A woman is taking a bath.',
'En jente børster håret sitt',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Semantic Similarity
- Dataset:
sts-dev
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.9551 |
spearman_cosine | 0.9593 |
pearson_manhattan | 0.927 |
spearman_manhattan | 0.9383 |
pearson_euclidean | 0.9278 |
spearman_euclidean | 0.9394 |
pearson_dot | 0.876 |
spearman_dot | 0.8865 |
pearson_max | 0.9551 |
spearman_max | 0.9593 |
Evalutation results vs SOTA results
- Dataset:
sts-test
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.948 |
spearman_cosine | 0.9515 |
pearson_manhattan | 0.9252 |
spearman_manhattan | 0.9352 |
pearson_euclidean | 0.9258 |
spearman_euclidean | 0.9364 |
pearson_dot | 0.8443 |
spearman_dot | 0.8435 |
pearson_max | 0.948 |
spearman_max | 0.9515 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 226,547 training samples
- Columns:
sentence_0
,sentence_1
, andlabel
- Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label type string string float details - min: 3 tokens
- mean: 20.05 tokens
- max: 128 tokens
- min: 4 tokens
- mean: 19.94 tokens
- max: 128 tokens
- min: 0.0
- mean: 1.92
- max: 398.6
- Samples:
sentence_0 sentence_1 label Bir kadın makineye dikiş dikiyor.
Bir kadın biraz et ekiyor.
0.12
Snowden 'gegeven vluchtelingendocument door Ecuador'.
Snowden staat op het punt om uit Moskou te vliegen
0.24000000953674316
Czarny pies idzie mostem przez wodę
Czarny pies nie idzie mostem przez wodę
0.74000000954
- Loss:
AnglELoss
with these parameters:{ "scale": 20.0, "similarity_fct": "pairwise_angle_sim" }
Training Hyperparameters
Non-Default Hyperparameters
per_device_train_batch_size
: 256per_device_eval_batch_size
: 256num_train_epochs
: 10multi_dataset_batch_sampler
: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseprediction_loss_only
: Trueper_device_train_batch_size
: 256per_device_eval_batch_size
: 256per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 10max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: round_robin
Training Logs
Epoch | Step | Training Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
---|---|---|---|---|
0.5650 | 500 | 10.9426 | - | - |
1.0 | 885 | - | 0.9202 | - |
1.1299 | 1000 | 9.7184 | - | - |
1.6949 | 1500 | 9.5348 | - | - |
2.0 | 1770 | - | 0.9400 | - |
2.2599 | 2000 | 9.4412 | - | - |
2.8249 | 2500 | 9.3097 | - | - |
3.0 | 2655 | - | 0.9489 | - |
3.3898 | 3000 | 9.2357 | - | - |
3.9548 | 3500 | 9.1594 | - | - |
4.0 | 3540 | - | 0.9528 | - |
4.5198 | 4000 | 9.0963 | - | - |
5.0 | 4425 | - | 0.9553 | - |
5.0847 | 4500 | 9.0382 | - | - |
5.6497 | 5000 | 8.9837 | - | - |
6.0 | 5310 | - | 0.9567 | - |
6.2147 | 5500 | 8.9403 | - | - |
6.7797 | 6000 | 8.8841 | - | - |
7.0 | 6195 | - | 0.9581 | - |
7.3446 | 6500 | 8.8513 | - | - |
7.9096 | 7000 | 8.81 | - | - |
8.0 | 7080 | - | 0.9582 | - |
8.4746 | 7500 | 8.8069 | - | - |
9.0 | 7965 | - | 0.9589 | - |
9.0395 | 8000 | 8.7616 | - | - |
9.6045 | 8500 | 8.7521 | - | - |
10.0 | 8850 | - | 0.9593 | 0.6266 |
Framework Versions
- Python: 3.9.7
- Sentence Transformers: 3.0.0
- Transformers: 4.40.1
- PyTorch: 2.3.0+cu121
- Accelerate: 0.29.3
- Datasets: 2.19.0
- 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",
}
AnglELoss
@misc{li2023angleoptimized,
title={AnglE-optimized Text Embeddings},
author={Xianming Li and Jing Li},
year={2023},
eprint={2309.12871},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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Model tree for Gameselo/STS-multilingual-mpnet-base-v2
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Evaluation results
- cosine_spearman on MTEB STS22test set self-reported0.685
- cosine_spearman on MTEB STS22test set self-reported0.662
- cosine_spearman on MTEB STS22test set self-reported0.788
- cosine_spearman on MTEB STS22test set self-reported0.751
- cosine_spearman on MTEB STS22test set self-reported0.627
- cosine_spearman on MTEB STS22test set self-reported0.434
- cosine_spearman on MTEB STS22test set self-reported0.577
- cosine_spearman on MTEB STS22test set self-reported0.638
- cosine_spearman on MTEB STS22test set self-reported0.662
- cosine_spearman on MTEB STS22test set self-reported0.587