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---
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language:
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- en
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library_name: sentence-transformers
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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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- loss:MultipleNegativesRankingLoss
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base_model: distilbert/distilroberta-base
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metrics:
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- pearson_cosine
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- spearman_cosine
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- pearson_manhattan
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- spearman_manhattan
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- pearson_euclidean
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- spearman_euclidean
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- pearson_dot
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- spearman_dot
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- pearson_max
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- spearman_max
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widget:
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- source_sentence: There's a dock
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sentences:
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- A boat docked on a river.
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- The girl is standing.
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- The boy is sleeping.
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- source_sentence: The boy scowls
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sentences:
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- The boy is smiling
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- A story book is open.
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- Two women are sleeping.
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- source_sentence: A bird flying.
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sentences:
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- an eagle flies
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- The person is amused.
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- Two men are sleeping.
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- source_sentence: an eagle flies
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sentences:
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- A butterfly flys freely.
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- Two men are sleeping.
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- Some men sleep.
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- source_sentence: A woman sings.
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sentences:
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- The woman is singing.
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- a man is wearing blue
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- The boy is sleeping.
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pipeline_tag: sentence-similarity
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co2_eq_emissions:
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emissions: 1.414068558007261
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energy_consumed: 0.003637924574628535
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source: codecarbon
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training_type: fine-tuning
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on_cloud: false
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cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
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ram_total_size: 31.777088165283203
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hours_used: 0.02
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hardware_used: 1 x NVIDIA GeForce RTX 3090
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model-index:
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- name: SentenceTransformer based on distilbert/distilroberta-base
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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: 0.7472500570689873
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name: Pearson Cosine
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- type: spearman_cosine
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value: 0.7815286852337371
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name: Spearman Cosine
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- type: pearson_manhattan
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value: 0.7466164303556344
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name: Pearson Manhattan
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- type: spearman_manhattan
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value: 0.7564406124153681
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name: Spearman Manhattan
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- type: pearson_euclidean
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value: 0.7470476982963574
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name: Pearson Euclidean
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- type: spearman_euclidean
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value: 0.7553538112024218
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name: Spearman Euclidean
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- type: pearson_dot
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value: 0.46791742113291
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name: Pearson Dot
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- type: spearman_dot
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value: 0.48306144010812363
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name: Spearman Dot
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- type: pearson_max
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value: 0.7472500570689873
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name: Pearson Max
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- type: spearman_max
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value: 0.7815286852337371
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name: Spearman Max
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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: 0.7145936155377322
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name: Pearson Cosine
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- type: spearman_cosine
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value: 0.7188509446042572
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name: Spearman Cosine
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- type: pearson_manhattan
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value: 0.7144637059488601
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name: Pearson Manhattan
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- type: spearman_manhattan
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value: 0.7051742909657058
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name: Spearman Manhattan
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- type: pearson_euclidean
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value: 0.7150126984629757
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name: Pearson Euclidean
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- type: spearman_euclidean
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value: 0.7054604043597239
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name: Spearman Euclidean
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- type: pearson_dot
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value: 0.4317482386066799
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name: Pearson Dot
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- type: spearman_dot
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value: 0.4292906929274994
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name: Spearman Dot
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- type: pearson_max
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value: 0.7150126984629757
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name: Pearson Max
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- type: spearman_max
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value: 0.7188509446042572
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name: Spearman Max
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---
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# SentenceTransformer based on distilbert/distilroberta-base
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
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- **Maximum Sequence Length:** 512 tokens
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- **Output Dimensionality:** 768 tokens
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- **Similarity Function:** Cosine Similarity
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- **Training Dataset:**
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- [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
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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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("tomaarsen/distilroberta-base-nli-v2")
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# Run inference
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sentences = [
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'A woman sings.',
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'The woman is singing.',
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'a man is wearing blue',
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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)
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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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## Evaluation
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### Metrics
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#### Semantic Similarity
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* Dataset: `sts-dev`
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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| Metric | Value |
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|:--------------------|:-----------|
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| pearson_cosine | 0.7473 |
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| **spearman_cosine** | **0.7815** |
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| pearson_manhattan | 0.7466 |
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| spearman_manhattan | 0.7564 |
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| pearson_euclidean | 0.747 |
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| spearman_euclidean | 0.7554 |
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| pearson_dot | 0.4679 |
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| spearman_dot | 0.4831 |
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| pearson_max | 0.7473 |
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| spearman_max | 0.7815 |
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#### Semantic Similarity
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* Dataset: `sts-test`
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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| Metric | Value |
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|:--------------------|:-----------|
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| pearson_cosine | 0.7146 |
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| **spearman_cosine** | **0.7189** |
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| pearson_manhattan | 0.7145 |
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| spearman_manhattan | 0.7052 |
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| pearson_euclidean | 0.715 |
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| spearman_euclidean | 0.7055 |
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| pearson_dot | 0.4317 |
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| spearman_dot | 0.4293 |
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| pearson_max | 0.715 |
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| spearman_max | 0.7189 |
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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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#### sentence-transformers/all-nli
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* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [cc6c526](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/cc6c526380e29912b5c6fa03682da4daf773c013)
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* Size: 10,000 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: 10.38 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 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>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
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```json
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{
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"scale": 20.0,
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"similarity_fct": "cos_sim"
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}
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```
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### Evaluation Dataset
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#### sentence-transformers/all-nli
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* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [cc6c526](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/cc6c526380e29912b5c6fa03682da4daf773c013)
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* Size: 1,000 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: 18.02 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.81 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.37 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>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters:
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```json
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{
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"scale": 20.0,
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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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- `eval_strategy`: steps
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- `per_device_train_batch_size`: 128
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- `per_device_eval_batch_size`: 128
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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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- `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`: False
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- `per_device_train_batch_size`: 128
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- `per_device_eval_batch_size`: 128
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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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- `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`: 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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- `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`: None
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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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- `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_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 | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
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|:------:|:----:|:------:|:-----------------------:|:------------------------:|
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| 0 | 0 | - | 0.6375 | - |
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| 0.1266 | 10 | 2.9835 | 0.7807 | - |
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| 0.2532 | 20 | 1.7048 | 0.7782 | - |
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| 0.3797 | 30 | 1.6657 | 0.7847 | - |
|
|
| 0.5063 | 40 | 1.7352 | 0.7900 | - |
|
|
| 0.6329 | 50 | 1.6400 | 0.7863 | - |
|
|
| 0.7595 | 60 | 1.7281 | 0.7820 | - |
|
|
| 0.8861 | 70 | 1.7066 | 0.7815 | - |
|
|
| 1.0 | 79 | - | - | 0.7189 |
|
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|
|
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### Environmental Impact
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Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
|
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- **Energy Consumed**: 0.004 kWh
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- **Carbon Emitted**: 0.001 kg of CO2
|
|
- **Hours Used**: 0.02 hours
|
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|
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### Training Hardware
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|
- **On Cloud**: No
|
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- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
|
|
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
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- **RAM Size**: 31.78 GB
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|
|
### Framework Versions
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- Python: 3.11.6
|
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- Sentence Transformers: 3.0.0.dev0
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- Transformers: 4.41.0.dev0
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- PyTorch: 2.3.0+cu121
|
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- Accelerate: 0.26.1
|
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- Datasets: 2.18.0
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- Tokenizers: 0.19.1
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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",
|
|
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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|
```
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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},
|
|
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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