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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:44114
- loss:ContrastiveLoss
widget:
- source_sentence: The Sadrist movement left the Alliance before the elections in
December 2005 , which also brought the Iraqi National Congress more firmly to
the Alliance .
sentences:
- The Iraqi National Congress left the Alliance before the December 2005 elections
, which also brought the Sadrist movement more to the Alliance .
- He pioneered important developments in the style of sculpting in wood , parallel
to those driven by Filippo Parodi in marble sculpture and Domenico Piola in painting
.
- The Mine South Deep is a large mine in the northern part of Gauteng in South Africa
.
- source_sentence: Mike Monroney was challenged by A.S. Thomas in the Democratic Prefix
in 1950 .
sentences:
- was challenged in 1950 by A.S. Mike Monroney in the Democratic Primary .
- The T helper cells then activate the B cells , which are also in the presence
of these antigens , causing the production of autoantibodies .
- Illinois Route 158 , or Washington Avenue , leads west to Columbia and east to
Belleville .
- source_sentence: Morrow can mean either the next day in particular or the future
in general .
sentences:
- Brockton is located approximately 25 miles northeast of Providence , Rhode Island
and 30 miles south of Boston .
- He had been in the state playing for Melbourne , but moved to Victoria in 1925
and appointed New Town .
- Morrow can either mean the next day in general , or the future in particular .
- source_sentence: Fotbal Club Forex Braşov was a Romanian professional club from
Braşov , Romania , who was founded in October 2002 and was dissolved in 2011 .
sentences:
- Fotbal Club Forex Braşov was a Romanian professional club from Braşov , Romania
, which was dissolved in October 2002 and was founded in 2011 .
- Nate decides to struggle for Ricky and confirms his love for her .
- Ricardo Lingan Baccay was ordained a priest on April 10 , 1987 by Diosdado Aenlle
Talamayan .
- source_sentence: He was born in July 1973 in Petroupoli ( Athens ) .
sentences:
- Carmen Aub Romero ( born October 24 , 1989 in Mexico City , DF , Mexico ) is a
Mexican actress .
- He was born in Athens in July 1973 ( Petroupoli ) .
- At the age of nine , Garcia appeared in his first concert and since then has appeared
alone or with his aunt and his uncle in all parts of France .
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: paws val deberta
type: paws-val-deberta
metrics:
- type: cosine_accuracy
value: 0.9121457489878543
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8481842279434204
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9024280575539567
name: Cosine F1
- type: cosine_f1_threshold
value: 0.8432618379592896
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.8860927152317881
name: Cosine Precision
- type: cosine_recall
value: 0.9193770041227668
name: Cosine Recall
- type: cosine_ap
value: 0.9503471324249102
name: Cosine Ap
- type: cosine_mcc
value: 0.8230430822451054
name: Cosine Mcc
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. 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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 64 tokens
- **Output Dimensionality:** 768 dimensions
- **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/huggingface/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': 64, 'do_lower_case': False, 'architecture': 'DebertaV2Model'})
(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("sentence_transformers_model_id")
# Run inference
sentences = [
'He was born in July 1973 in Petroupoli ( Athens ) .',
'He was born in Athens in July 1973 ( Petroupoli ) .',
'At the age of nine , Garcia appeared in his first concert and since then has appeared alone or with his aunt and his uncle in all parts of France .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9386, 0.5843],
# [0.9386, 1.0000, 0.5614],
# [0.5843, 0.5614, 1.0000]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Binary Classification
* Dataset: `paws-val-deberta`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:--------------------------|:-----------|
| cosine_accuracy | 0.9121 |
| cosine_accuracy_threshold | 0.8482 |
| cosine_f1 | 0.9024 |
| cosine_f1_threshold | 0.8433 |
| cosine_precision | 0.8861 |
| cosine_recall | 0.9194 |
| **cosine_ap** | **0.9503** |
| cosine_mcc | 0.823 |
<!--
## Bias, Risks and Limitations
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 44,114 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: 8 tokens</li><li>mean: 25.39 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 25.47 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>The Song of Ceylon is a 1934 British documentary film produced by Basil Wright and directed by John Grierson for the Ceylon Tea Propaganda Board .</code> | <code>The Song of Ceylon is a British documentary film directed by Basil Wright by John Grierson for the Ceylon Tea Propaganda Board in 1934 .</code> | <code>0.0</code> |
| <code>The two leased aircraft were returned to the BAE Systems lessor on 9 November 2006 .</code> | <code>Centavia 's two leased aircraft were returned to the lessor , BAE Systems , on November 9 , 2006 .</code> | <code>1.0</code> |
| <code>When , in 1818 , Ortona was assigned to Lanciano , Campli was joined to the diocese of Teramo .</code> | <code>When Ortona was assigned to Lanciano in 1818 , Campli was connected to the Diocese of Teramo .</code> | <code>1.0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 2
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `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`: 2
- `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
- `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}
- `parallelism_config`: 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
- `project`: huggingface
- `trackio_space_id`: trackio
- `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`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | paws-val-deberta_cosine_ap |
|:------:|:----:|:-------------:|:--------------------------:|
| 0.1813 | 500 | 0.0314 | - |
| 0.3626 | 1000 | 0.023 | - |
| 0.5439 | 1500 | 0.0188 | - |
| 0.7252 | 2000 | 0.0161 | - |
| 0.9065 | 2500 | 0.0148 | - |
| 1.0 | 2758 | - | 0.9361 |
| 1.0877 | 3000 | 0.0121 | - |
| 1.2690 | 3500 | 0.0107 | - |
| 1.4503 | 4000 | 0.01 | - |
| 1.6316 | 4500 | 0.0098 | - |
| 1.8129 | 5000 | 0.0094 | - |
| 1.9942 | 5500 | 0.0091 | - |
| 2.0 | 5516 | - | 0.9503 |
### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.2.0
- Transformers: 4.57.3
- PyTorch: 2.9.0+cu126
- Accelerate: 1.12.0
- Datasets: 4.0.0
- Tokenizers: 0.22.1
## 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",
}
```
#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}
```
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