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--- |
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language: |
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- en |
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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:11002 |
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- loss:MultipleNegativesRankingLoss |
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base_model: BAAI/bge-base-en-v1.5 |
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widget: |
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- source_sentence: Man jumps alone on a desert road with mountains in the background. |
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sentences: |
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- A man jumps on the desert road |
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- A man plays a silver electric guitar. |
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- A man doesnt jump on the desert road |
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- source_sentence: Players from two teams tangle together in pursuit of a flying rugby |
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ball. |
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sentences: |
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- Two teams playing. |
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- Two teams not playing. |
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- Men are dancing in the street. |
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- source_sentence: The team won the game in the final minute. |
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sentences: |
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- In the final minute, the team won the game. |
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- The team lost the game in the final minute. |
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- For their anniversary, they took a hike through the mountains, enjoying the peace |
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and quiet of nature. |
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- source_sentence: He finished reading the book in one sitting. |
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sentences: |
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- He struggled to finish the book and took a week to read it. |
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- In one sitting, he finished reading the book. |
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- jazz players create spontaneous superior orchestra |
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- source_sentence: Paint preserves wood |
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sentences: |
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- Coating protects timber |
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- timber coating protects |
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- Single cell life came before complex creatures |
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datasets: |
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- bwang0911/word-orders-triplet |
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- jinaai/negation-dataset |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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--- |
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# SentenceTransformer based on BAAI/bge-base-en-v1.5 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [word_orders](https://huggingface.co/datasets/bwang0911/word-orders-triplet) and [negation_dataset](https://huggingface.co/datasets/jinaai/negation-dataset) datasets. It maps sentences & paragraphs to a 768-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-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Datasets:** |
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- [word_orders](https://huggingface.co/datasets/bwang0911/word-orders-triplet) |
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- [negation_dataset](https://huggingface.co/datasets/jinaai/negation-dataset) |
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- **Language:** en |
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<!-- - **License:** Unknown --> |
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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': 128, 'do_lower_case': True}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, '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("bwang0911/word-order-bge") |
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# Run inference |
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sentences = [ |
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'Paint preserves wood', |
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'Coating protects timber', |
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'timber coating protects', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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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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<!-- |
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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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### 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 Datasets |
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#### word_orders |
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* Dataset: [word_orders](https://huggingface.co/datasets/bwang0911/word-orders-triplet) at [99609ac](https://huggingface.co/datasets/bwang0911/word-orders-triplet/tree/99609ac84ce5ad127591d7e722564a064cf80a76) |
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* Size: 1,002 training samples |
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* Columns: <code>anchor</code>, <code>pos</code>, and <code>neg</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | pos | neg | |
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|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 12.34 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.1 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.51 tokens</li><li>max: 24 tokens</li></ul> | |
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* Samples: |
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| anchor | pos | neg | |
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|:-----------------------------------------------------------|:----------------------------------------------------------|:-----------------------------------------------------------| |
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| <code>The river flows from the mountains to the sea</code> | <code>Water travels from mountain peaks to ocean</code> | <code>The river flows from the sea to the mountains</code> | |
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| <code>Train departs London for Paris</code> | <code>Railway journey from London heading to Paris</code> | <code>Train departs Paris for London</code> | |
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| <code>Cargo ship sails from Shanghai to Singapore</code> | <code>Maritime route Shanghai to Singapore</code> | <code>Cargo ship sails from Singapore to Shanghai</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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#### negation_dataset |
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* Dataset: [negation_dataset](https://huggingface.co/datasets/jinaai/negation-dataset) at [cd02256](https://huggingface.co/datasets/jinaai/negation-dataset/tree/cd02256426cc566d176285a987e5436f1cd01382) |
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* Size: 10,000 training samples |
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* Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | entailment | 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: 16.48 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.63 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.46 tokens</li><li>max: 32 tokens</li></ul> | |
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* Samples: |
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| anchor | entailment | negative | |
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|:-------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------|:---------------------------------------------------------| |
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| <code>Two young girls are playing outside in a non-urban environment.</code> | <code>Two girls are playing outside.</code> | <code>Two girls are not playing outside.</code> | |
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| <code>A man with a red shirt is watching another man who is standing on top of a attached cart filled to the top.</code> | <code>A man is standing on top of a cart.</code> | <code>A man is not standing on top of a cart.</code> | |
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| <code>A man in a blue shirt driving a Segway type vehicle.</code> | <code>A person is riding a motorized vehicle.</code> | <code>A person is not riding a motorized vehicle.</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20, |
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"similarity_fct": "cos_sim" |
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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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- `per_device_train_batch_size`: 256 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: 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`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 8 |
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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`: 5e-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 |
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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`: False |
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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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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | |
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|:------:|:----:|:-------------:| |
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| 0.2273 | 10 | 1.6158 | |
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| 0.4545 | 20 | 1.1681 | |
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| 0.6818 | 30 | 0.8775 | |
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| 0.9091 | 40 | 0.7628 | |
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| 1.1364 | 50 | 1.0154 | |
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| 1.3636 | 60 | 0.7048 | |
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| 1.5909 | 70 | 0.7981 | |
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| 1.8182 | 80 | 0.6322 | |
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| 2.0455 | 90 | 0.4916 | |
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| 2.2727 | 100 | 0.8441 | |
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| 2.5 | 110 | 0.6697 | |
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| 2.7273 | 120 | 0.5358 | |
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| 2.9545 | 130 | 0.5111 | |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.3.1 |
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- Transformers: 4.46.0 |
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- PyTorch: 2.5.1+cu124 |
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- Accelerate: 1.1.1 |
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- Datasets: 3.1.0 |
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- Tokenizers: 0.20.1 |
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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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#### 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}, |
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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}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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