README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:80
- loss:CoSENTLoss
base_model: abdeljalilELmajjodi/model
widget:
- source_sentence: The school is having a special event in order to show the american
    culture on how other cultures are dealt with in parties.
  sentences:
  - A school is hosting an event.
  - A child with mom and dad, on summer vacation at the beach.
  - There are people just getting on a train
- source_sentence: Two women who just had lunch hugging and saying goodbye.
  sentences:
  - Two adults walk across a street.
  - A team is trying to tag a runner out.
  - There are two woman in this picture.
- source_sentence: A boy is jumping on skateboard in the middle of a red bridge.
  sentences:
  - The boy does a skateboarding trick.
  - The two sisters saw each other across the crowded diner and shared a hug, both
    clutching their doggie bags.
  - The women do not care what clothes they wear.
- source_sentence: Woman in white in foreground and a man slightly behind walking
    with a sign for John's Pizza and Gyro in the background.
  sentences:
  - A woman in white.
  - The woman is wearing white.
  - Some people board a train.
- source_sentence: A woman is walking across the street eating a banana, while a man
    is following with his briefcase.
  sentences:
  - A man in a restaurant is waiting for his meal to arrive.
  - A married couple is sleeping.
  - An actress and her favorite assistant talk a walk in the city.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on abdeljalilELmajjodi/model
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: pair score evaluator dev
      type: pair-score-evaluator-dev
    metrics:
    - type: pearson_cosine
      value: 0.3132380731765732
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.21958363099039482
      name: Spearman Cosine
---

# SentenceTransformer based on abdeljalilELmajjodi/model

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [abdeljalilELmajjodi/model](https://huggingface.co/abdeljalilELmajjodi/model) on the all-nli dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [abdeljalilELmajjodi/model](https://huggingface.co/abdeljalilELmajjodi/model) <!-- at revision 284169e2c18b482372374a251b8dc1e1756416de -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Supported Modality:** Text
- **Training Dataset:**
    - all-nli
<!-- - **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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'mean', '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 = [
    'A woman is walking across the street eating a banana, while a man is following with his briefcase.',
    'An actress and her favorite assistant talk a walk in the city.',
    'A man in a restaurant is waiting for his meal to arrive.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9891, 0.9925],
#         [0.9891, 1.0000, 0.9870],
#         [0.9925, 0.9870, 1.0000]])
```
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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Evaluation

### Metrics

#### Semantic Similarity

* Dataset: `pair-score-evaluator-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.3132     |
| **spearman_cosine** | **0.2196** |

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## Training Details

### Training Dataset

#### all-nli

* Dataset: all-nli
* Size: 80 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 80 samples:
  |         | sentence1                                                                          | sentence2                                                                         | score                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | float                                                          |
  | details | <ul><li>min: 10 tokens</li><li>mean: 25.59 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.04 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                                                                                                | sentence2                                                                                | score            |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:-----------------|
  | <code>Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background.</code>                     | <code>The woman and man are outdoors.</code>                                             | <code>1.0</code> |
  | <code>An older man sits with his orange juice at a small table in a coffee shop while employees in bright colored shirts smile in the background.</code> | <code>An older man drinks his juice as he waits for his daughter to get off work.</code> | <code>0.5</code> |
  | <code>Children smiling and waving at camera</code>                                                                                                       | <code>There are children present</code>                                                  | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Evaluation Dataset

#### all-nli

* Dataset: all-nli
* Size: 20 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 20 samples:
  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                          |
  | details | <ul><li>min: 14 tokens</li><li>mean: 26.3 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 11.75 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.65</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                                                                                | sentence2                                                  | score            |
  |:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------|:-----------------|
  | <code>High fashion ladies wait outside a tram beside a crowd of people in the city.</code>                                               | <code>The women do not care what clothes they wear.</code> | <code>0.0</code> |
  | <code>The school is having a special event in order to show the american culture on how other cultures are dealt with in parties.</code> | <code>A school is hosting an event.</code>                 | <code>1.0</code> |
  | <code>An older man is drinking orange juice at a restaurant.</code>                                                                      | <code>A man is drinking juice.</code>                      | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `num_train_epochs`: 1
- `warmup_steps`: 0.05
- `bf16`: True
- `fp16_full_eval`: True
- `load_best_model_at_end`: True
- `push_to_hub`: True
- `gradient_checkpointing`: True

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `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`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_ratio`: None
- `warmup_steps`: 0.05
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `enable_jit_checkpoint`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `use_cpu`: False
- `seed`: 42
- `data_seed`: None
- `bf16`: True
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: True
- `tf32`: None
- `local_rank`: -1
- `ddp_backend`: None
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `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
- `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
- `push_to_hub`: True
- `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`: True
- `gradient_checkpointing_kwargs`: None
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `auto_find_batch_size`: False
- `full_determinism`: False
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `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
- `use_cache`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch   | Step   | Training Loss | Validation Loss | pair-score-evaluator-dev_spearman_cosine |
|:-------:|:------:|:-------------:|:---------------:|:----------------------------------------:|
| 0.1     | 1      | 3.0315        | -               | -                                        |
| 0.5     | 5      | 3.0018        | -               | -                                        |
| **1.0** | **10** | **3.7124**    | **2.6509**      | **0.2737**                               |
| 0.1     | 1      | 2.8043        | -               | -                                        |
| 0.5     | 5      | 2.9111        | -               | -                                        |
| **1.0** | **10** | **3.1121**    | **2.6923**      | **0.2196**                               |

* The bold row denotes the saved checkpoint.

### Training Time
- **Training**: 5.6 minutes

### Framework Versions
- Python: 3.12.13
- Sentence Transformers: 5.4.1
- Transformers: 5.0.0
- PyTorch: 2.10.0+cu128
- Accelerate: 1.13.0
- Datasets: 4.8.5
- Tokenizers: 0.22.2

## 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
@article{10531646,
    author={Huang, Xiang and Peng, Hao and Zou, Dongcheng and Liu, Zhiwei and Li, Jianxin and Liu, Kay and Wu, Jia and Su, Jianlin and Yu, Philip S.},
    journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
    title={CoSENT: Consistent Sentence Embedding via Similarity Ranking},
    year={2024},
    doi={10.1109/TASLP.2024.3402087}
}
```

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