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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:1195425 |
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- loss:MSELoss |
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base_model: mixedbread-ai/mxbai-embed-large-v1 |
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widget: |
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- source_sentence: >- |
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At an outdoor event in an Asian-themed area, a crowd congregates as one |
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person in a yellow Chinese dragon costume confronts the camera. |
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sentences: |
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- Boy dressed in blue holds a toy. |
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- A man is smiling at his wife. |
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- Two young asian men are squatting. |
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- source_sentence: A man with a shopping cart is studying the shelves in a supermarket aisle. |
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sentences: |
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- the animal is running |
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- The children are watching TV at home. |
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- >- |
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Three young boys one is holding a camera and another is holding a green toy |
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all are wearing t-shirt and smiling. |
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- source_sentence: The door is open. |
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sentences: |
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- A girl is using an apple laptop with her headphones in her ears. |
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- >- |
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There are three men in this picture, two are on motorbikes, one of the men |
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has a large piece of furniture on the back of his bike, the other is about |
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to be handed a piece of paper by a man in a white shirt. |
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- >- |
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A large group of people are gathered outside of a brick building lit with |
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spotlights. |
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- source_sentence: >- |
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A small group of children are standing in a classroom and one of them has a |
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foot in a trashcan, which also has a rope leading out of it. |
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sentences: |
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- People are playing music. |
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- Children are swimming at the beach. |
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- Women are celebrating at a bar. |
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- source_sentence: >- |
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A black dog is drinking next to a brown and white dog that is looking at an |
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orange ball in the lake, whilst a horse and rider passes behind. |
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sentences: |
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- Some men with jerseys are in a bar, watching a soccer match. |
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- the guy is dead |
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- >- |
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There are two people running around a track in lane three and the one |
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wearing a blue shirt with a green thing over the eyes is just barely ahead |
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of the guy wearing an orange shirt and sunglasses. |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- negative_mse |
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model-index: |
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- name: SentenceTransformer based on mixedbread-ai/mxbai-embed-large-v1 |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts dev |
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type: sts-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.8654028138219636 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8873087539713633 |
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name: Spearman Cosine |
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- task: |
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type: knowledge-distillation |
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name: Knowledge Distillation |
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dataset: |
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name: Unknown |
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type: unknown |
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metrics: |
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- type: negative_mse |
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value: -3.3795181661844254 |
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name: Negative Mse |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts test |
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type: sts-test |
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metrics: |
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- type: pearson_cosine |
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value: 0.834023412201456 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8723901159121923 |
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name: Spearman Cosine |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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# SentenceTransformer based on Model Distillation |
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In this experiment with knowledge distillation for embedding models, i retained 8 layers from the teacher model. This is an attempt to create a lighter, faster version. |
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- the top left graph shows how well your model's predictions match reality. Spearman correlation = 0.887 |
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- the top right compares the correlation performance of this model vs the reference(mxbai-embed-large-v1) model - both bars around 0.8-0.9 |
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- bottom left shows, this model processes about 45 samples/s and mxbai-embed-large-v1 processes about 30 samples/s. |
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- the bottom right shows a small accuracy drop for this model. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) <!-- at revision e7857440379da569f68f19e8403b69cd7be26e50 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 1024 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'A black dog is drinking next to a brown and white dog that is looking at an orange ball in the lake, whilst a horse and rider passes behind.', |
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'Some men with jerseys are in a bar, watching a soccer match.', |
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'There are two people running around a track in lane three and the one wearing a blue shirt with a green thing over the eyes is just barely ahead of the guy wearing an orange shirt and sunglasses.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Datasets: `sts-dev` and `sts-test` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | sts-dev | sts-test | |
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|:--------------------|:-----------|:-----------| |
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| pearson_cosine | 0.8654 | 0.834 | |
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| **spearman_cosine** | **0.8873** | **0.8724** | |
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#### Knowledge Distillation |
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* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator) |
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| Metric | Value | |
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|:-----------------|:------------| |
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| **negative_mse** | **-3.3795** | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 1,195,425 training samples |
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* Columns: <code>sentence</code> and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence | label | |
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|:--------|:----------------------------------------------------------------------------------|:--------------------------------------| |
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| type | string | list | |
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| details | <ul><li>min: 4 tokens</li><li>mean: 12.24 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>size: 1024 elements</li></ul> | |
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* Samples: |
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| sentence | label | |
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|:---------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------| |
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| <code>A person on a horse jumps over a broken down airplane.</code> | <code>[-0.012967385351657867, 0.3716000020503998, 0.252520889043808, 0.7052643299102783, -0.15118499100208282, ...]</code> | |
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| <code>Children smiling and waving at camera</code> | <code>[0.15414997935295105, 0.6666896939277649, -0.3150098919868469, 1.0102407932281494, 0.4113735556602478, ...]</code> | |
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| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>[-0.2989530563354492, 0.8571284413337708, -0.48532426357269287, 0.8935043215751648, 0.28524795174598694, ...]</code> | |
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* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) |
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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 10,000 evaluation samples |
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* Columns: <code>sentence</code> and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence | label | |
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|:--------|:----------------------------------------------------------------------------------|:--------------------------------------| |
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| type | string | list | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 13.23 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>size: 1024 elements</li></ul> | |
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* Samples: |
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| sentence | label | |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------| |
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| <code>Two women are embracing while holding to go packages.</code> | <code>[-0.35094621777534485, 0.4337681233882904, 0.22905530035495758, 0.9438946843147278, -1.0199058055877686, ...]</code> | |
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| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>[-0.37593328952789307, 0.6690596342086792, -0.14921458065509796, 0.7559019923210144, -0.4093412756919861, ...]</code> | |
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| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>[0.21969863772392273, 0.5065202713012695, -0.25664886832237244, 0.2569092810153961, -0.05940837413072586, ...]</code> | |
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* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `learning_rate`: 0.0001 |
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- `num_train_epochs`: 1 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `load_best_model_at_end`: True |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 0.0001 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Framework Versions |
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- Python: 3.10.14 |
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- Sentence Transformers: 3.3.1 |
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- Transformers: 4.46.3 |
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- PyTorch: 2.4.0 |
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- Accelerate: 1.1.1 |
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- Datasets: 3.1.0 |
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- Tokenizers: 0.20.3 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MSELoss |
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```bibtex |
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@inproceedings{reimers-2020-multilingual-sentence-bert, |
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title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2020", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/2004.09813", |
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} |
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``` |
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