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---
language: []
library_name: sentence-transformers
tags:
- mteb
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:AnglELoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
- source_sentence: 有些人在路上溜达。
sentences:
- Folk går
- Otururken gitar çalan adam.
- ארה"ב קבעה שסוריה השתמשה בנשק כימי
- source_sentence: 緬甸以前稱為緬甸。
sentences:
- 缅甸以前叫缅甸。
- This is very contradictory.
- 한 남자가 아기를 안고 의자에 앉아 잠들어 있다.
- source_sentence: אדם כותב.
sentences:
- האדם כותב.
- questa non è una risposta.
- 7 שוטרים נהרגו ו-4 שוטרים נפצעו.
- source_sentence: הם מפחדים.
sentences:
- liên quan đến rủi ro đáng kể;
- A man is playing a guitar.
- A man is playing a piano.
- source_sentence: 一个女人正在洗澡。
sentences:
- A woman is taking a bath.
- En jente børster håret sitt
- אדם מחלק תפוח אדמה.
pipeline_tag: sentence-similarity
---
## 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](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/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](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 79f2382ceacceacdf38563d7c5d16b9ff8d725d6 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
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("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]
```
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You can finetune this model on your own dataset.
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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 [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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 |
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 226,547 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 20.05 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.94 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 1.92</li><li>max: 398.6</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:-------------------------------------------------------------------|:----------------------------------------------------------------|:---------------------------------|
| <code>Bir kadın makineye dikiş dikiyor.</code> | <code>Bir kadın biraz et ekiyor.</code> | <code>0.12</code> |
| <code>Snowden 'gegeven vluchtelingendocument door Ecuador'.</code> | <code>Snowden staat op het punt om uit Moskou te vliegen</code> | <code>0.24000000953674316</code> |
| <code>Czarny pies idzie mostem przez wodę</code> | <code>Czarny pies nie idzie mostem przez wodę</code> | <code>0.74000000954</code> |
* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_angle_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `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
- `num_train_epochs`: 10
- `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
- `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, '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_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### 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
```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",
}
```
#### AnglELoss
```bibtex
@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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