---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:80
- loss:CoSENTLoss
base_model: abdeljalilELmajjodi/model
widget:
- source_sentence: A woman wearing all white and eating, walks next to a man holding
    a briefcase.
  sentences:
  - A married couple is sleeping.
  - The people are eating omelettes.
  - Two adults walk across a street.
- 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:
  - There are people waiting on a train.
  - The family is outside.
  - The man with the sign is caucasian.
- source_sentence: Two blond women are hugging one another.
  sentences:
  - The women are sleeping.
  - Olympic swimming.
  - A couple are playing frisbee with a young child at the beach.
- 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:
  - The woman is wearing black.
  - They are smiling at their parents
  - The woman and man are playing baseball together.
- source_sentence: A woman is walking across the street eating a banana, while a man
    is following with his briefcase.
  sentences:
  - A school is hosting an event.
  - There are people just getting on a train
  - An actress and her favorite assistant talk a walk in the city.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# 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 school is hosting an event.',
]
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, 0.9934, 0.9887],
#         [0.9934, 1.0000, 0.9916],
#         [0.9887, 0.9916, 1.0000]])
```
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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: 25.77 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.45 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                                                      | sentence2                                                                                       | score            |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-----------------|
  | <code>Two adults, one female in white, with shades and one male, gray clothes, walking across a street, away from a eatery with a blurred image of a dark colored red shirted person in the foreground.</code> | <code>Two adults walk across the street.</code>                                                 | <code>1.0</code> |
  | <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 man is sitting down while he has a sign for John's Pizza and Gyro in his arms.</code> | <code>0.0</code> |
  | <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> |
* 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: 10 tokens</li><li>mean: 25.55 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.1 tokens</li><li>max: 17 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>A couple play in the tide with their young son.</code> | <code>The family is outside.</code>            | <code>1.0</code> |
  | <code>Two blond women are hugging one another.</code>        | <code>The women are sleeping.</code>           | <code>0.0</code> |
  | <code>Children smiling and waving at camera</code>           | <code>They are smiling at their parents</code> | <code>0.5</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"
  }
  ```

### 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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