| --- |
| 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]]) |
| ``` |
| <!-- |
| ### 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> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## 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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