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---
base_model: silma-ai/silma-embeddding-matryoshka-0.1
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:34436
- loss:CosineSimilarityLoss
widget:
- source_sentence: Three men are playing chess.
sentences:
- Two men are fighting.
- امرأة تحمل و تحمل طفل كنغر
- Two men are playing chess.
- source_sentence: Two men are playing chess.
sentences:
- رجل يعزف على الغيتار و يغني
- Three men are playing chess.
- طائرة طيران تقلع
- source_sentence: Two men are playing chess.
sentences:
- A man is playing a large flute. رجل يعزف على ناي كبير
- The man is playing the piano. الرجل يعزف على البيانو
- Three men are playing chess.
- source_sentence: الرجل يعزف على البيانو The man is playing the piano.
sentences:
- رجل يجلس ويلعب الكمان A man seated is playing the cello.
- ثلاثة رجال يلعبون الشطرنج.
- الرجل يعزف على الغيتار The man is playing the guitar.
- source_sentence: الرجل ضرب الرجل الآخر بعصا The man hit the other man with a stick.
sentences:
- الرجل صفع الرجل الآخر بعصا The man spanked the other man with a stick.
- A plane is taking off.
- A man is smoking. رجل يدخن
model-index:
- name: SentenceTransformer based on silma-ai/silma-embeddding-matryoshka-0.1
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 512
type: sts-dev-512
metrics:
- type: pearson_cosine
value: 0.8509127994264242
name: Pearson Cosine
- type: spearman_cosine
value: 0.8548500966032416
name: Spearman Cosine
- type: pearson_manhattan
value: 0.821303728669975
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8364598068079891
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8210450198328316
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8382181658285147
name: Spearman Euclidean
- type: pearson_dot
value: 0.8491261828772604
name: Pearson Dot
- type: spearman_dot
value: 0.8559811107036664
name: Spearman Dot
- type: pearson_max
value: 0.8509127994264242
name: Pearson Max
- type: spearman_max
value: 0.8559811107036664
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 256
type: sts-dev-256
metrics:
- type: pearson_cosine
value: 0.8498025312190702
name: Pearson Cosine
- type: spearman_cosine
value: 0.8530609768738506
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8181745876468085
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8328727236454085
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8193792688284338
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8338632184708783
name: Spearman Euclidean
- type: pearson_dot
value: 0.8396368156921546
name: Pearson Dot
- type: spearman_dot
value: 0.8484397673758116
name: Spearman Dot
- type: pearson_max
value: 0.8498025312190702
name: Pearson Max
- type: spearman_max
value: 0.8530609768738506
name: Spearman Max
---
# SentenceTransformer based on silma-ai/silma-embeddding-matryoshka-0.1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [silma-ai/silma-embeddding-matryoshka-0.1](https://huggingface.co/silma-ai/silma-embeddding-matryoshka-0.1). 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:** [silma-ai/silma-embeddding-matryoshka-0.1](https://huggingface.co/silma-ai/silma-embeddding-matryoshka-0.1) <!-- at revision 9eb50734f432656a01e1f88d28fa9a6fe8b9e148 -->
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: BertModel
(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("silma-ai/silma-embeddding-sts-0.1")
# Run inference
sentences = [
'الرجل ضرب الرجل الآخر بعصا The man hit the other man with a stick.',
'الرجل صفع الرجل الآخر بعصا The man spanked the other man with a stick.',
'A man is smoking. رجل يدخن',
]
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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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8509 |
| **spearman_cosine** | **0.8549** |
| pearson_manhattan | 0.8213 |
| spearman_manhattan | 0.8365 |
| pearson_euclidean | 0.821 |
| spearman_euclidean | 0.8382 |
| pearson_dot | 0.8491 |
| spearman_dot | 0.856 |
| pearson_max | 0.8509 |
| spearman_max | 0.856 |
#### Semantic Similarity
* Dataset: `sts-dev-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8498 |
| **spearman_cosine** | **0.8531** |
| pearson_manhattan | 0.8182 |
| spearman_manhattan | 0.8329 |
| pearson_euclidean | 0.8194 |
| spearman_euclidean | 0.8339 |
| pearson_dot | 0.8396 |
| spearman_dot | 0.8484 |
| pearson_max | 0.8498 |
| spearman_max | 0.8531 |
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 34,436 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 4 tokens</li><li>mean: 15.18 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.18 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:------------------|
| <code>A woman picks up and holds a baby kangaroo in her arms. امرأة تحمل في ذراعها طفل كنغر</code> | <code>A woman picks up and holds a baby kangaroo. امرأة تحمل و تحمل طفل كنغر</code> | <code>0.92</code> |
| <code>امرأة تحمل و تحمل طفل كنغر A woman picks up and holds a baby kangaroo.</code> | <code>امرأة تحمل في ذراعها طفل كنغر A woman picks up and holds a baby kangaroo in her arms.</code> | <code>0.92</code> |
| <code>رجل يعزف على الناي</code> | <code>رجل يعزف على فرقة الخيزران</code> | <code>0.77</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 100 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 100 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 4 tokens</li><li>mean: 15.96 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.96 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.72</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------|:-----------------------------------------|:-----------------|
| <code>طائرة ستقلع</code> | <code>طائرة طيران تقلع</code> | <code>1.0</code> |
| <code>طائرة طيران تقلع</code> | <code>طائرة ستقلع</code> | <code>1.0</code> |
| <code>A plane is taking off.</code> | <code>An air plane is taking off.</code> | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 250
- `per_device_eval_batch_size`: 10
- `learning_rate`: 1e-06
- `num_train_epochs`: 10
- `bf16`: True
- `dataloader_drop_last`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 250
- `per_device_eval_batch_size`: 10
- `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`: 1e-06
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `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
- `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`: True
- `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`: True
- `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_fused
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | sts-dev-512_spearman_cosine | sts-dev-256_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:---------------------------:|:---------------------------:|
| 0.3650 | 50 | 0.0395 | 0.0424 | 0.8486 | 0.8487 |
| 0.7299 | 100 | 0.031 | 0.0427 | 0.8493 | 0.8495 |
| 1.0949 | 150 | 0.0344 | 0.0430 | 0.8496 | 0.8496 |
| 1.4599 | 200 | 0.0313 | 0.0427 | 0.8506 | 0.8504 |
| 1.8248 | 250 | 0.0267 | 0.0428 | 0.8504 | 0.8506 |
| 2.1898 | 300 | 0.0309 | 0.0429 | 0.8516 | 0.8515 |
| 2.5547 | 350 | 0.0276 | 0.0425 | 0.8531 | 0.8521 |
| 2.9197 | 400 | 0.028 | 0.0426 | 0.8530 | 0.8515 |
| 3.2847 | 450 | 0.0281 | 0.0425 | 0.8539 | 0.8521 |
| 3.6496 | 500 | 0.0248 | 0.0425 | 0.8542 | 0.8523 |
| 4.0146 | 550 | 0.0302 | 0.0424 | 0.8541 | 0.8520 |
| 4.3796 | 600 | 0.0261 | 0.0421 | 0.8545 | 0.8523 |
| 4.7445 | 650 | 0.0233 | 0.0420 | 0.8544 | 0.8522 |
| 5.1095 | 700 | 0.0281 | 0.0419 | 0.8547 | 0.8528 |
| 5.4745 | 750 | 0.0257 | 0.0419 | 0.8546 | 0.8531 |
| 5.8394 | 800 | 0.0235 | 0.0418 | 0.8546 | 0.8527 |
| 6.2044 | 850 | 0.0268 | 0.0418 | 0.8551 | 0.8529 |
| 6.5693 | 900 | 0.0238 | 0.0416 | 0.8552 | 0.8526 |
| 6.9343 | 950 | 0.0255 | 0.0416 | 0.8549 | 0.8526 |
| 7.2993 | 1000 | 0.0253 | 0.0416 | 0.8548 | 0.8528 |
| 7.6642 | 1050 | 0.0225 | 0.0415 | 0.8550 | 0.8525 |
| 8.0292 | 1100 | 0.0276 | 0.0414 | 0.8550 | 0.8528 |
| 8.3942 | 1150 | 0.0244 | 0.0415 | 0.8550 | 0.8533 |
| 8.7591 | 1200 | 0.0218 | 0.0414 | 0.8551 | 0.8529 |
| 9.1241 | 1250 | 0.0263 | 0.0414 | 0.8550 | 0.8531 |
| 9.4891 | 1300 | 0.0241 | 0.0414 | 0.8552 | 0.8533 |
| 9.8540 | 1350 | 0.0227 | 0.0415 | 0.8549 | 0.8531 |
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.2.0
- Transformers: 4.45.2
- PyTorch: 2.3.1
- Accelerate: 1.0.1
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## 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",
}
```
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