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---
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language:
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- en
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license: apache-2.0
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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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- generated_with_trainer
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- dataset_size:100K<n<1M
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- loss:SoftmaxLoss
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- loss:CosineSimilarityLoss
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base_model: microsoft/mpnet-base
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datasets:
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- nyu-mll/multi_nli
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- stanfordnlp/snli
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- mteb/stsbenchmark-sts
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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: A taxi SUV drives past an urban construction site, as a man walks
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down the street in the other direction.
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sentences:
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- The woman is walking down the street with high heels.
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- A man is reading documents in a binder.
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- A man is chasing an SUV that is going in the same direction as him.
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- source_sentence: Young man running towards a tennis court while another is waiting
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in the other side of the net.
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sentences:
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- The person is cooking a hamburger.
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- A young man is running to grab a tennis ball.
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- A woman is dancing near a fire.
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- source_sentence: An asian woman sitting outside an outdoor market stall.
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sentences:
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- There are three workers
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- A woman sits outdoors.
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- Five women sit at a table.
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- source_sentence: All the same methods of analysis that are used with spoken languages
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apply successfully to signed languages.
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sentences:
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- One idea that's been going around at least since the 80s is that you can distinguish
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between Holds and Moves.
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- You only need two-dimensional trigonometry if you know the distances to the two
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stars and their angular separation.
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- A woman driving a car is talking to the man seated beside her.
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- source_sentence: Rouen is the ancient center of Normandy's thriving textile industry,
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and the place of Joan of Arc's martyrdom ' a national symbol of resistance to
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tyranny.
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sentences:
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- The islands are part of France now instead of just colonies.
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- Joan of Arc sacrificed her life at Rouen, which became an enduring symbol of opposition
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to tyranny.
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- I don't know how cold it got last night.
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pipeline_tag: sentence-similarity
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co2_eq_emissions:
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emissions: 6.863209894681815
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energy_consumed: 0.017656739339344318
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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: 0.068
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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 microsoft/mpnet-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: 0.8344104750902503
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name: Pearson Cosine
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- type: spearman_cosine
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value: 0.8294923795333993
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name: Spearman Cosine
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- type: pearson_manhattan
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value: 0.8316959259914674
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name: Pearson Manhattan
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- type: spearman_manhattan
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value: 0.8331844817222047
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name: Spearman Manhattan
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- type: pearson_euclidean
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value: 0.8272941934077804
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name: Pearson Euclidean
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- type: spearman_euclidean
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value: 0.8294923795333993
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name: Spearman Euclidean
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- type: pearson_dot
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value: 0.8344104825648291
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name: Pearson Dot
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- type: spearman_dot
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value: 0.8294923795333993
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name: Spearman Dot
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- type: pearson_max
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value: 0.8344104825648291
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name: Pearson Max
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- type: spearman_max
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value: 0.8331844817222047
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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: 0.7776062173443514
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name: Pearson Cosine
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- type: spearman_cosine
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value: 0.7642518713703523
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name: Spearman Cosine
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- type: pearson_manhattan
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value: 0.7788269653910183
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name: Pearson Manhattan
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- type: spearman_manhattan
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value: 0.7659203139768728
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name: Spearman Manhattan
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- type: pearson_euclidean
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value: 0.7763456809736229
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name: Pearson Euclidean
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- type: spearman_euclidean
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value: 0.7642518713703523
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name: Spearman Euclidean
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- type: pearson_dot
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value: 0.7776062158976489
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name: Pearson Dot
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- type: spearman_dot
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value: 0.7642518713703523
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name: Spearman Dot
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- type: pearson_max
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value: 0.7788269653910183
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name: Pearson Max
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- type: spearman_max
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value: 0.7659203139768728
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name: Spearman Max
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---
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# SentenceTransformer based on microsoft/mpnet-base
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [multi_nli](https://huggingface.co/datasets/nyu-mll/multi_nli), [snli](https://huggingface.co/datasets/stanfordnlp/snli) and [stsb](https://huggingface.co/datasets/mteb/stsbenchmark-sts) 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
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- **Maximum Sequence Length:** 384 tokens
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- **Output Dimensionality:** 768 tokens
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- **Similarity Function:** Dot Product
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- **Training Datasets:**
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- [multi_nli](https://huggingface.co/datasets/nyu-mll/multi_nli)
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- [snli](https://huggingface.co/datasets/stanfordnlp/snli)
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- [stsb](https://huggingface.co/datasets/mteb/stsbenchmark-sts)
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- **Language:** en
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- **License:** apache-2.0
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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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
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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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(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("tomaarsen/mpnet-base-allnli")
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# Run inference
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sentences = [
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"Rouen is the ancient center of Normandy's thriving textile industry, and the place of Joan of Arc's martyrdom ' a national symbol of resistance to tyranny.",
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'Joan of Arc sacrificed her life at Rouen, which became an enduring symbol of opposition to tyranny.',
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'The islands are part of France now instead of just colonies.',
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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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## 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/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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| Metric | Value |
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|:-------------------|:-----------|
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| pearson_cosine | 0.8344 |
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| spearman_cosine | 0.8295 |
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| pearson_manhattan | 0.8317 |
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| spearman_manhattan | 0.8332 |
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| pearson_euclidean | 0.8273 |
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| spearman_euclidean | 0.8295 |
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| pearson_dot | 0.8344 |
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| **spearman_dot** | **0.8295** |
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| pearson_max | 0.8344 |
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| spearman_max | 0.8332 |
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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/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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| Metric | Value |
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|:--------------------|:-----------|
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| pearson_cosine | 0.7776 |
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| **spearman_cosine** | **0.7643** |
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| pearson_manhattan | 0.7788 |
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| spearman_manhattan | 0.7659 |
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| pearson_euclidean | 0.7763 |
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| spearman_euclidean | 0.7643 |
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| pearson_dot | 0.7776 |
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| spearman_dot | 0.7643 |
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| pearson_max | 0.7788 |
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| spearman_max | 0.7659 |
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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 Datasets
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#### multi_nli
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* Dataset: [multi_nli](https://huggingface.co/datasets/nyu-mll/multi_nli) at [da70db2](https://huggingface.co/datasets/nyu-mll/multi_nli/tree/da70db2af9d09693783c3320c4249840212ee221)
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* Size: 392,702 training samples
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* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | premise | hypothesis | label |
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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
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| type | string | string | int |
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| details | <ul><li>min: 4 tokens</li><li>mean: 26.95 tokens</li><li>max: 189 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.11 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>0: ~34.30%</li><li>1: ~28.20%</li><li>2: ~37.50%</li></ul> |
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* Samples:
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| premise | hypothesis | label |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------|
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| <code>Conceptually cream skimming has two basic dimensions - product and geography.</code> | <code>Product and geography are what make cream skimming work. </code> | <code>1</code> |
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| <code>you know during the season and i guess at at your level uh you lose them to the next level if if they decide to recall the the parent team the Braves decide to call to recall a guy from triple A then a double A guy goes up to replace him and a single A guy goes up to replace him</code> | <code>You lose the things to the following level if the people recall.</code> | <code>0</code> |
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| <code>One of our number will carry out your instructions minutely.</code> | <code>A member of my team will execute your orders with immense precision.</code> | <code>0</code> |
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* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
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#### snli
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* Dataset: [snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
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* Size: 549,367 training samples
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* Columns: <code>snli_premise</code>, <code>hypothesis</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | snli_premise | hypothesis | label |
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|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------|
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| type | string | string | int |
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| details | <ul><li>min: 6 tokens</li><li>mean: 17.38 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.7 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>0: ~33.40%</li><li>1: ~33.30%</li><li>2: ~33.30%</li></ul> |
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* Samples:
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| snli_premise | hypothesis | label |
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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 training his horse for a competition.</code> | <code>1</code> |
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| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is at a diner, ordering an omelette.</code> | <code>2</code> |
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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>0</code> |
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* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
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#### stsb
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* Dataset: [stsb](https://huggingface.co/datasets/mteb/stsbenchmark-sts) at [8913289](https://huggingface.co/datasets/mteb/stsbenchmark-sts/tree/8913289635987208e6e7c72789e4be2fe94b6abd)
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* Size: 5,749 training samples
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | sentence1 | sentence2 | label |
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|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
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| type | string | string | float |
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| details | <ul><li>min: 6 tokens</li><li>mean: 10.0 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.95 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
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* Samples:
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| sentence1 | sentence2 | label |
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|:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------|
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| <code>A plane is taking off.</code> | <code>An air plane is taking off.</code> | <code>1.0</code> |
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| <code>A man is playing a large flute.</code> | <code>A man is playing a flute.</code> | <code>0.76</code> |
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| <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</code> |
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* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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```json
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{
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"loss_fct": "torch.nn.modules.loss.MSELoss"
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}
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```
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|
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### Evaluation Datasets
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#### multi_nli
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* Dataset: [multi_nli](https://huggingface.co/datasets/nyu-mll/multi_nli) at [da70db2](https://huggingface.co/datasets/nyu-mll/multi_nli/tree/da70db2af9d09693783c3320c4249840212ee221)
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* Size: 100 evaluation samples
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* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | premise | hypothesis | label |
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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
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| type | string | string | int |
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| details | <ul><li>min: 5 tokens</li><li>mean: 27.67 tokens</li><li>max: 138 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.48 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>0: ~35.00%</li><li>1: ~31.00%</li><li>2: ~34.00%</li></ul> |
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* Samples:
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| premise | hypothesis | label |
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|:---------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:---------------|
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| <code>The new rights are nice enough</code> | <code>Everyone really likes the newest benefits </code> | <code>1</code> |
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| <code>This site includes a list of all award winners and a searchable database of Government Executive articles.</code> | <code>The Government Executive articles housed on the website are not able to be searched.</code> | <code>2</code> |
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| <code>uh i don't know i i have mixed emotions about him uh sometimes i like him but at the same times i love to see somebody beat him</code> | <code>I like him for the most part, but would still enjoy seeing someone beat him.</code> | <code>0</code> |
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* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
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#### snli
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* Dataset: [snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
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* Size: 9,842 evaluation samples
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* Columns: <code>snli_premise</code>, <code>hypothesis</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
|
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| | snli_premise | hypothesis | label |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
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| type | string | string | int |
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| details | <ul><li>min: 6 tokens</li><li>mean: 18.44 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.57 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>0: ~33.10%</li><li>1: ~33.30%</li><li>2: ~33.60%</li></ul> |
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* Samples:
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| snli_premise | hypothesis | label |
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|:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------|
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| <code>Two women are embracing while holding to go packages.</code> | <code>The sisters are hugging goodbye while holding to go packages after just eating lunch.</code> | <code>1</code> |
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| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>0</code> |
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| <code>Two women are embracing while holding to go packages.</code> | <code>The men are fighting outside a deli.</code> | <code>2</code> |
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* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
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#### stsb
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* Dataset: [stsb](https://huggingface.co/datasets/mteb/stsbenchmark-sts) at [8913289](https://huggingface.co/datasets/mteb/stsbenchmark-sts/tree/8913289635987208e6e7c72789e4be2fe94b6abd)
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* Size: 1,500 evaluation samples
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | sentence1 | sentence2 | label |
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|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
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| type | string | string | float |
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| details | <ul><li>min: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
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* Samples:
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| sentence1 | sentence2 | label |
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|:--------------------------------------------------|:------------------------------------------------------|:------------------|
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| <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> |
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| <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> |
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| <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> |
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* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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```json
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{
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"loss_fct": "torch.nn.modules.loss.MSELoss"
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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`: 64
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- `per_device_eval_batch_size`: 64
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- `learning_rate`: 2e-05
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- `num_train_epochs`: 1
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- `warmup_ratio`: 0.1
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- `seed`: 33
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- `bf16`: True
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- `load_best_model_at_end`: True
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- `push_to_hub`: True
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- `hub_model_id`: tomaarsen/mpnet-base-allnli
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- `hub_private_repo`: True
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- `multi_dataset_batch_sampler`: round_robin
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#### All Hyperparameters
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<details><summary>Click to expand</summary>
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- `overwrite_output_dir`: False
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- `do_predict`: False
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- `eval_strategy`: steps
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- `prediction_loss_only`: True
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- `per_device_train_batch_size`: 64
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- `per_device_eval_batch_size`: 64
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- `per_gpu_train_batch_size`: None
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- `per_gpu_eval_batch_size`: None
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- `gradient_accumulation_steps`: 1
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- `eval_accumulation_steps`: None
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- `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`: 1
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- `max_steps`: -1
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- `lr_scheduler_type`: linear
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- `lr_scheduler_kwargs`: {}
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- `warmup_ratio`: 0.1
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- `warmup_steps`: 0
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- `log_level`: passive
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- `log_level_replica`: warning
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- `log_on_each_node`: True
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- `logging_nan_inf_filter`: True
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- `save_safetensors`: True
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- `save_on_each_node`: False
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- `save_only_model`: False
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- `restore_callback_states_from_checkpoint`: False
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- `no_cuda`: False
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- `use_cpu`: False
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- `use_mps_device`: False
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- `seed`: 33
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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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- `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`: True
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- `resume_from_checkpoint`: None
|
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- `hub_model_id`: tomaarsen/mpnet-base-allnli
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- `hub_strategy`: every_save
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- `hub_private_repo`: True
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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
|
|
- `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
|
|
- `batch_sampler`: batch_sampler
|
|
- `multi_dataset_batch_sampler`: round_robin
|
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|
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</details>
|
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|
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### Training Logs
|
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| Epoch | Step | Training Loss | multi nli loss | snli loss | stsb loss | sts-dev_spearman_dot | sts-test_spearman_cosine |
|
|
|:----------:|:-------:|:-------------:|:--------------:|:----------:|:----------:|:--------------------:|:------------------------:|
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| 0.0370 | 10 | 0.8347 | - | - | - | - | - |
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| 0.0741 | 20 | 0.8269 | - | - | - | - | - |
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| 0.1111 | 30 | 0.7036 | 1.0978 | 1.0984 | 0.0830 | 0.6636 | - |
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| 0.1481 | 40 | 0.7889 | - | - | - | - | - |
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| 0.1852 | 50 | 0.7948 | - | - | - | - | - |
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| 0.2222 | 60 | 0.688 | 1.0976 | 1.0961 | 0.0679 | 0.7124 | - |
|
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| 0.2593 | 70 | 0.7911 | - | - | - | - | - |
|
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| 0.2963 | 80 | 0.7847 | - | - | - | - | - |
|
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| 0.3333 | 90 | 0.6801 | 1.0950 | 1.0942 | 0.0522 | 0.7810 | - |
|
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| 0.3704 | 100 | 0.7837 | - | - | - | - | - |
|
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| 0.4074 | 110 | 0.7803 | - | - | - | - | - |
|
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| 0.4444 | 120 | 0.6756 | 1.0978 | 1.0929 | 0.0441 | 0.8157 | - |
|
|
| 0.4815 | 130 | 0.7829 | - | - | - | - | - |
|
|
| 0.5185 | 140 | 0.7789 | - | - | - | - | - |
|
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| 0.5556 | 150 | 0.6756 | 1.0954 | 1.0911 | 0.0433 | 0.8215 | - |
|
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| 0.5926 | 160 | 0.7802 | - | - | - | - | - |
|
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| 0.6296 | 170 | 0.7751 | - | - | - | - | - |
|
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| 0.6667 | 180 | 0.6679 | 1.0934 | 1.0885 | 0.0401 | 0.8235 | - |
|
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| 0.7037 | 190 | 0.7755 | - | - | - | - | - |
|
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| 0.7407 | 200 | 0.775 | - | - | - | - | - |
|
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| **0.7778** | **210** | **0.6694** | **1.0919** | **1.0859** | **0.0377** | **0.8295** | **-** |
|
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| 0.8148 | 220 | 0.7733 | - | - | - | - | - |
|
|
| 0.8519 | 230 | 0.772 | - | - | - | - | - |
|
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| 0.8889 | 240 | 0.6656 | 1.0891 | 1.0838 | 0.0365 | 0.8292 | - |
|
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| 0.9259 | 250 | 0.7726 | - | - | - | - | - |
|
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| 0.9630 | 260 | 0.7731 | - | - | - | - | - |
|
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| 1.0 | 270 | 0.6674 | 1.0888 | 1.0833 | 0.0372 | 0.8295 | 0.7643 |
|
|
|
|
* The bold row denotes the saved checkpoint.
|
|
|
|
### Environmental Impact
|
|
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
|
|
- **Energy Consumed**: 0.018 kWh
|
|
- **Carbon Emitted**: 0.007 kg of CO2
|
|
- **Hours Used**: 0.068 hours
|
|
|
|
### Training Hardware
|
|
- **On Cloud**: No
|
|
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
|
|
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
|
|
- **RAM Size**: 31.78 GB
|
|
|
|
### Framework Versions
|
|
- Python: 3.11.6
|
|
- Sentence Transformers: 3.1.0.dev0
|
|
- Transformers: 4.41.2
|
|
- PyTorch: 2.3.0+cu121
|
|
- Accelerate: 0.30.1
|
|
- Datasets: 2.19.1
|
|
- Tokenizers: 0.19.1
|
|
|
|
## Citation
|
|
|
|
### BibTeX
|
|
|
|
#### Sentence Transformers and SoftmaxLoss
|
|
```bibtex
|
|
@inproceedings{reimers-2019-sentence-bert,
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
|
author = "Reimers, Nils and Gurevych, Iryna",
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
|
month = "11",
|
|
year = "2019",
|
|
publisher = "Association for Computational Linguistics",
|
|
url = "https://arxiv.org/abs/1908.10084",
|
|
}
|
|
```
|
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