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--- |
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language: |
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- en |
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license: mit |
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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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- dense |
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- generated_from_trainer |
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- dataset_size:200000 |
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- loss:AdaptiveLayerLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: BAAI/bge-large-en-v1.5 |
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widget: |
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- source_sentence: A man standing in front of a brick building. |
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sentences: |
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- The men are together. |
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- A man is outside. |
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- The man pushes a women on the ground. |
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- source_sentence: A football coach is walking on a football field. |
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sentences: |
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- Two girls are watching dolls. |
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- a baseball player walks on the field |
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- a football coach walks on the field |
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- source_sentence: A woman wearing gray pants, a white blouse and a black vest is |
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jumping with one hand in the air as she goes through an indoor stadium. |
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sentences: |
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- The girl wearing a dress skips down the sidewalk. |
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- They are outdoors. |
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- The jumping lady in slacks also has her hand raised. |
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- source_sentence: A light brown dog with his tail in the air jumps of a pontoon toward |
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the water. |
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sentences: |
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- A man is heading to his house of worship. |
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- A dog jumps toward the water. |
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- A cat is jumping in the air. |
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- source_sentence: Young boy kicks a soccer ball towards the goal as the crowd watches. |
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sentences: |
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- The boy is under the age of eighteen. |
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- The girl is running. |
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- The boy is alone in his backyard. |
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datasets: |
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- sentence-transformers/all-nli |
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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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- cosine_accuracy |
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model-index: |
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- name: bge-large-en-v1.5 |
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results: |
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- task: |
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type: triplet |
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name: Triplet |
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dataset: |
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name: all nli val |
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type: all-nli-val |
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metrics: |
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- type: cosine_accuracy |
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value: 0.9513333439826965 |
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name: Cosine Accuracy |
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- task: |
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type: triplet |
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name: Triplet |
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dataset: |
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name: all nli test |
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type: all-nli-test |
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metrics: |
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- type: cosine_accuracy |
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value: 0.9527916312217712 |
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name: Cosine Accuracy |
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--- |
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# bge-large-en-v1.5 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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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:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 --> |
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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:** |
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- [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) |
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- **Language:** en |
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- **License:** mit |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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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': True, 'architecture': '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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(2): Normalize() |
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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("DannyAI/embedding_fine_tuning_adaptive_layer_bge-large-en-v1.5") |
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# Run inference |
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sentences = [ |
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'Young boy kicks a soccer ball towards the goal as the crowd watches.', |
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'The boy is under the age of eighteen.', |
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'The boy is alone in his backyard.', |
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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) |
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# tensor([[1.0000, 0.5172, 0.2682], |
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# [0.5172, 1.0000, 0.4388], |
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# [0.2682, 0.4388, 1.0000]]) |
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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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--> |
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## Evaluation |
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### Metrics |
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#### Triplet |
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* Datasets: `all-nli-val` and `all-nli-test` |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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| Metric | all-nli-val | all-nli-test | |
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|:--------------------|:------------|:-------------| |
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| **cosine_accuracy** | **0.9513** | **0.9528** | |
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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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<!-- |
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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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#### all-nli |
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* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) |
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* Size: 200,000 training samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| |
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| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> | |
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| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> | |
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| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> | |
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* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"n_layers_per_step": 1, |
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"last_layer_weight": 1.0, |
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"prior_layers_weight": 1.0, |
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"kl_div_weight": 1.0, |
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"kl_temperature": 0.3 |
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} |
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``` |
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### Evaluation Dataset |
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#### all-nli |
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* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) |
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* Size: 3,000 evaluation samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 17.95 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.78 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.35 tokens</li><li>max: 29 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------| |
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| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</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>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</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>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> | |
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* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"n_layers_per_step": 1, |
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"last_layer_weight": 1.0, |
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"prior_layers_weight": 1.0, |
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"kl_div_weight": 1.0, |
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"kl_temperature": 0.3 |
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} |
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``` |
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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`: 5 |
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- `per_device_eval_batch_size`: 5 |
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- `learning_rate`: 2e-05 |
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- `max_steps`: 600 |
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- `warmup_ratio`: 0.1 |
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- `seed`: 30 |
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- `bf16`: True |
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- `load_best_model_at_end`: True |
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- `batch_sampler`: no_duplicates |
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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`: 5 |
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- `per_device_eval_batch_size`: 5 |
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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`: 2e-05 |
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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`: 3.0 |
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- `max_steps`: 600 |
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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`: 30 |
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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`: True |
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- `fp16`: False |
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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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- `parallelism_config`: None |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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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`: None |
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- `hub_always_push`: False |
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- `hub_revision`: None |
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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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- `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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- `liger_kernel_config`: None |
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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`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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- `router_mapping`: {} |
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- `learning_rate_mapping`: {} |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | Validation Loss | all-nli-val_cosine_accuracy | all-nli-test_cosine_accuracy | |
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|:---------:|:-------:|:-------------:|:---------------:|:---------------------------:|:----------------------------:| |
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| -1 | -1 | - | - | 0.9600 | - | |
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| 0.0025 | 100 | 0.7326 | 0.5273 | 0.9520 | - | |
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| 0.005 | 200 | 0.6625 | 0.5954 | 0.9490 | - | |
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| 0.0075 | 300 | 0.6088 | 0.5250 | 0.9487 | - | |
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| 0.01 | 400 | 0.5053 | 0.5289 | 0.9550 | - | |
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| 0.0125 | 500 | 0.6039 | 0.5101 | 0.9483 | - | |
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| **0.015** | **600** | **0.5923** | **0.5037** | **0.9513** | **-** | |
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| -1 | -1 | - | - | - | 0.9528 | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.12.11 |
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- Sentence Transformers: 5.1.0 |
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- Transformers: 4.56.1 |
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- PyTorch: 2.8.0+cu126 |
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- Accelerate: 1.10.1 |
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- Datasets: 4.0.0 |
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- Tokenizers: 0.22.0 |
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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", |
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = "11", |
|
|
year = "2019", |
|
|
publisher = "Association for Computational Linguistics", |
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|
url = "https://arxiv.org/abs/1908.10084", |
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|
} |
|
|
``` |
|
|
|
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#### AdaptiveLayerLoss |
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|
```bibtex |
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|
@misc{li20242d, |
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|
title={2D Matryoshka Sentence Embeddings}, |
|
|
author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li}, |
|
|
year={2024}, |
|
|
eprint={2402.14776}, |
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|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL} |
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|
} |
|
|
``` |
|
|
|
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|
#### MultipleNegativesRankingLoss |
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|
```bibtex |
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|
@misc{henderson2017efficient, |
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|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
|
year={2017}, |
|
|
eprint={1705.00652}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL} |
|
|
} |
|
|
``` |
|
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|
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