|
--- |
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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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- generated |
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base_model: microsoft/mpnet-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: 'Really? No kidding! ' |
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sentences: |
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- yeah really no kidding |
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- At the end of the fourth century was when baked goods flourished. |
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- The campaigns seem to reach a new pool of contributors. |
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- source_sentence: A sleeping man. |
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sentences: |
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- Two men are sleeping. |
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- Someone is selling oranges |
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- the family is young |
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- source_sentence: a guy on a bike |
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sentences: |
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- A tall person on a bike |
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- A man is on a frozen lake. |
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- The women throw food at the kids |
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- source_sentence: yeah really no kidding |
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sentences: |
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- oh uh-huh well no they wouldn't would they no |
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- yeah i mean just when uh the they military paid for her education |
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- The campaigns seem to reach a new pool of contributors. |
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- source_sentence: He ran like an athlete. |
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sentences: |
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- ' Then he ran.' |
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- yeah i mean just when uh the they military paid for her education |
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- Similarly, OIM revised the electronic Grant Renewal Application to accommodate |
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new information sought by LSC and to ensure greater ease for users. |
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pipeline_tag: sentence-similarity |
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co2_eq_emissions: |
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emissions: 17.515467907816664 |
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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: 0.13 |
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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: 0.7331234146933103 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.7435439430716654 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.7389474504545281 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.7473580293303098 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.7356264396007131 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
|
value: 0.7436137284782617 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.7093073700072118 |
|
name: Pearson Dot |
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- type: spearman_dot |
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value: 0.7150453113301433 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.7389474504545281 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.7473580293303098 |
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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: 0.6750510843835755 |
|
name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.6615639695746663 |
|
name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.6718085205234632 |
|
name: Pearson Manhattan |
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- type: spearman_manhattan |
|
value: 0.6589482932175834 |
|
name: Spearman Manhattan |
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- type: pearson_euclidean |
|
value: 0.6693170762111229 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.6578210069410166 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
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value: 0.6490291380804283 |
|
name: Pearson Dot |
|
- type: spearman_dot |
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value: 0.6335192601696299 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.6750510843835755 |
|
name: Pearson Max |
|
- type: spearman_max |
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value: 0.6615639695746663 |
|
name: Spearman Max |
|
--- |
|
|
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# SentenceTransformer based on microsoft/mpnet-base |
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|
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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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|
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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) |
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- **Maximum Sequence Length:** 384 tokens |
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- **Output Dimensionality:** 768 tokens |
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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:** Unknown --> |
|
|
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### Model Sources |
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|
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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) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
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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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) |
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``` |
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|
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## Usage |
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|
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### Direct Usage (Sentence Transformers) |
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|
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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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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|
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# Download from the 🤗 Hub |
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model = SentenceTransformer("tomaarsen/st-v3-test-mpnet-base-allnli-stsb") |
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# Run inference |
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sentences = [ |
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"He ran like an athlete.", |
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" Then he ran.", |
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"yeah i mean just when uh the they military paid for her education", |
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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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``` |
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|
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<!-- |
|
### Direct Usage (Transformers) |
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|
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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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<!-- |
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### Downstream Usage (Sentence Transformers) |
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|
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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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<!-- |
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### Out-of-Scope Use |
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|
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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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|
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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) |
|
|
|
| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.7331 | |
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| **spearman_cosine** | **0.7435** | |
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| pearson_manhattan | 0.7389 | |
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| spearman_manhattan | 0.7474 | |
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| pearson_euclidean | 0.7356 | |
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| spearman_euclidean | 0.7436 | |
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| pearson_dot | 0.7093 | |
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| spearman_dot | 0.715 | |
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| pearson_max | 0.7389 | |
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| spearman_max | 0.7474 | |
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|
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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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|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.6751 | |
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| **spearman_cosine** | **0.6616** | |
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| pearson_manhattan | 0.6718 | |
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| spearman_manhattan | 0.6589 | |
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| pearson_euclidean | 0.6693 | |
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| spearman_euclidean | 0.6578 | |
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| pearson_dot | 0.649 | |
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| spearman_dot | 0.6335 | |
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| pearson_max | 0.6751 | |
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| spearman_max | 0.6616 | |
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|
|
<!-- |
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## Bias, Risks and Limitations |
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|
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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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|
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## Training Details |
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|
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### Training Datasets |
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|
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#### multi_nli |
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|
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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: 10,000 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: |
|
| | premise | hypothesis | label | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------| |
|
| 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> | |
|
* Samples: |
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| premise | hypothesis | label | |
|
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------| |
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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> | |
|
| <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> | |
|
| <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>sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss</code>](https://sbert.net/docs/package_reference/losses.html#softmaxloss) |
|
|
|
#### snli |
|
|
|
* Dataset: [snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b) |
|
* Size: 10,000 training samples |
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* Columns: <code>snli_premise</code>, <code>hypothesis</code>, and <code>label</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | snli_premise | hypothesis | label | |
|
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------| |
|
| type | string | string | int | |
|
| 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> | |
|
* Samples: |
|
| snli_premise | hypothesis | label | |
|
|:--------------------------------------------------------------------|:---------------------------------------------------------------|:---------------| |
|
| <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> | |
|
| <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>sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss</code>](https://sbert.net/docs/package_reference/losses.html#softmaxloss) |
|
|
|
#### stsb |
|
|
|
* Dataset: [stsb](https://huggingface.co/datasets/mteb/stsbenchmark-sts) at [8913289](https://huggingface.co/datasets/mteb/stsbenchmark-sts/tree/8913289635987208e6e7c72789e4be2fe94b6abd) |
|
* Size: 5,749 training samples |
|
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | label | |
|
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| |
|
| type | string | string | float | |
|
| 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> | |
|
* Samples: |
|
| sentence1 | sentence2 | label | |
|
|:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------| |
|
| <code>A plane is taking off.</code> | <code>An air plane is taking off.</code> | <code>1.0</code> | |
|
| <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>sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters: |
|
```json |
|
{ |
|
"loss_fct": "torch.nn.modules.loss.MSELoss" |
|
} |
|
``` |
|
|
|
### Evaluation Datasets |
|
|
|
#### multi_nli |
|
|
|
* Dataset: [multi_nli](https://huggingface.co/datasets/nyu-mll/multi_nli) at [da70db2](https://huggingface.co/datasets/nyu-mll/multi_nli/tree/da70db2af9d09693783c3320c4249840212ee221) |
|
* Size: 100 evaluation samples |
|
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | premise | hypothesis | label | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------| |
|
| type | string | string | int | |
|
| 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> | |
|
* Samples: |
|
| premise | hypothesis | label | |
|
|:---------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:---------------| |
|
| <code>The new rights are nice enough</code> | <code>Everyone really likes the newest benefits </code> | <code>1</code> | |
|
| <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> | |
|
| <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> | |
|
* Loss: [<code>sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss</code>](https://sbert.net/docs/package_reference/losses.html#softmaxloss) |
|
|
|
#### snli |
|
|
|
* Dataset: [snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b) |
|
* Size: 9,842 evaluation samples |
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* Columns: <code>snli_premise</code>, <code>hypothesis</code>, and <code>label</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | snli_premise | hypothesis | label | |
|
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------| |
|
| type | string | string | int | |
|
| 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> | |
|
* Samples: |
|
| snli_premise | hypothesis | label | |
|
|:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------| |
|
| <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> | |
|
| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>0</code> | |
|
| <code>Two women are embracing while holding to go packages.</code> | <code>The men are fighting outside a deli.</code> | <code>2</code> | |
|
* Loss: [<code>sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss</code>](https://sbert.net/docs/package_reference/losses.html#softmaxloss) |
|
|
|
#### stsb |
|
|
|
* Dataset: [stsb](https://huggingface.co/datasets/mteb/stsbenchmark-sts) at [8913289](https://huggingface.co/datasets/mteb/stsbenchmark-sts/tree/8913289635987208e6e7c72789e4be2fe94b6abd) |
|
* Size: 1,500 evaluation samples |
|
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | label | |
|
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
|
| type | string | string | float | |
|
| 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> | |
|
* Samples: |
|
| sentence1 | sentence2 | label | |
|
|:--------------------------------------------------|:------------------------------------------------------|:------------------| |
|
| <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> | |
|
| <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> | |
|
| <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> | |
|
* Loss: [<code>sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters: |
|
```json |
|
{ |
|
"loss_fct": "torch.nn.modules.loss.MSELoss" |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- per_device_train_batch_size: 128 |
|
- per_device_eval_batch_size: 128 |
|
- learning_rate: 2e-05 |
|
- num_train_epochs: 1 |
|
- warmup_ratio: 0.1 |
|
- seed: 33 |
|
- bf16: True |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- overwrite_output_dir: False |
|
- do_predict: False |
|
- prediction_loss_only: False |
|
- per_device_train_batch_size: 128 |
|
- per_device_eval_batch_size: 128 |
|
- per_gpu_train_batch_size: None |
|
- per_gpu_eval_batch_size: None |
|
- gradient_accumulation_steps: 1 |
|
- eval_accumulation_steps: None |
|
- learning_rate: 2e-05 |
|
- weight_decay: 0.0 |
|
- adam_beta1: 0.9 |
|
- adam_beta2: 0.999 |
|
- adam_epsilon: 1e-08 |
|
- max_grad_norm: 1.0 |
|
- num_train_epochs: 1 |
|
- max_steps: -1 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_kwargs: {} |
|
- warmup_ratio: 0.1 |
|
- warmup_steps: 0 |
|
- log_level: passive |
|
- log_level_replica: warning |
|
- log_on_each_node: True |
|
- logging_nan_inf_filter: True |
|
- save_safetensors: True |
|
- save_on_each_node: False |
|
- save_only_model: False |
|
- no_cuda: False |
|
- use_cpu: False |
|
- use_mps_device: False |
|
- seed: 33 |
|
- data_seed: None |
|
- jit_mode_eval: False |
|
- use_ipex: False |
|
- bf16: True |
|
- fp16: False |
|
- fp16_opt_level: O1 |
|
- half_precision_backend: auto |
|
- bf16_full_eval: False |
|
- fp16_full_eval: False |
|
- tf32: None |
|
- local_rank: 0 |
|
- ddp_backend: None |
|
- tpu_num_cores: None |
|
- tpu_metrics_debug: False |
|
- debug: [] |
|
- dataloader_drop_last: False |
|
- dataloader_num_workers: 0 |
|
- dataloader_prefetch_factor: None |
|
- past_index: -1 |
|
- disable_tqdm: False |
|
- remove_unused_columns: True |
|
- label_names: None |
|
- load_best_model_at_end: False |
|
- ignore_data_skip: False |
|
- fsdp: [] |
|
- fsdp_min_num_params: 0 |
|
- fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- fsdp_transformer_layer_cls_to_wrap: None |
|
- accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True} |
|
- deepspeed: None |
|
- label_smoothing_factor: 0.0 |
|
- optim: adamw_torch |
|
- optim_args: None |
|
- adafactor: False |
|
- group_by_length: False |
|
- length_column_name: length |
|
- ddp_find_unused_parameters: None |
|
- ddp_bucket_cap_mb: None |
|
- ddp_broadcast_buffers: None |
|
- dataloader_pin_memory: True |
|
- dataloader_persistent_workers: False |
|
- skip_memory_metrics: True |
|
- use_legacy_prediction_loop: False |
|
- push_to_hub: False |
|
- resume_from_checkpoint: None |
|
- hub_model_id: None |
|
- hub_strategy: every_save |
|
- hub_private_repo: False |
|
- hub_always_push: False |
|
- gradient_checkpointing: False |
|
- gradient_checkpointing_kwargs: None |
|
- include_inputs_for_metrics: False |
|
- fp16_backend: auto |
|
- push_to_hub_model_id: None |
|
- push_to_hub_organization: None |
|
- mp_parameters: |
|
- auto_find_batch_size: False |
|
- full_determinism: False |
|
- torchdynamo: None |
|
- ray_scope: last |
|
- ddp_timeout: 1800 |
|
- torch_compile: False |
|
- torch_compile_backend: None |
|
- torch_compile_mode: None |
|
- dispatch_batches: None |
|
- split_batches: None |
|
- include_tokens_per_second: False |
|
- include_num_input_tokens_seen: False |
|
- neftune_noise_alpha: None |
|
- optim_target_modules: None |
|
- round_robin_sampler: False |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | multi nli loss | snli loss | stsb loss | sts-dev spearman cosine | |
|
|:------:|:----:|:-------------:|:--------------:|:---------:|:---------:|:-----------------------:| |
|
| 0.0493 | 10 | 0.9199 | 1.1019 | 1.1017 | 0.3016 | 0.6324 | |
|
| 0.0985 | 20 | 1.0063 | 1.1000 | 1.0966 | 0.2635 | 0.6093 | |
|
| 0.1478 | 30 | 1.002 | 1.0995 | 1.0908 | 0.1766 | 0.5328 | |
|
| 0.1970 | 40 | 0.7946 | 1.0980 | 1.0913 | 0.0923 | 0.5991 | |
|
| 0.2463 | 50 | 0.9891 | 1.0967 | 1.0781 | 0.0912 | 0.6457 | |
|
| 0.2956 | 60 | 0.784 | 1.0938 | 1.0699 | 0.0934 | 0.6629 | |
|
| 0.3448 | 70 | 0.6735 | 1.0940 | 1.0728 | 0.0640 | 0.7538 | |
|
| 0.3941 | 80 | 0.7713 | 1.0893 | 1.0676 | 0.0612 | 0.7653 | |
|
| 0.4433 | 90 | 0.9772 | 1.0870 | 1.0573 | 0.0636 | 0.7621 | |
|
| 0.4926 | 100 | 0.8613 | 1.0862 | 1.0515 | 0.0632 | 0.7583 | |
|
| 0.5419 | 110 | 0.7528 | 1.0814 | 1.0397 | 0.0617 | 0.7536 | |
|
| 0.5911 | 120 | 0.6541 | 1.0854 | 1.0329 | 0.0657 | 0.7512 | |
|
| 0.6404 | 130 | 1.051 | 1.0658 | 1.0211 | 0.0607 | 0.7340 | |
|
| 0.6897 | 140 | 0.8516 | 1.0631 | 1.0171 | 0.0587 | 0.7467 | |
|
| 0.7389 | 150 | 0.7484 | 1.0563 | 1.0122 | 0.0556 | 0.7537 | |
|
| 0.7882 | 160 | 0.7368 | 1.0534 | 1.0100 | 0.0588 | 0.7526 | |
|
| 0.8374 | 170 | 0.8373 | 1.0498 | 1.0030 | 0.0565 | 0.7491 | |
|
| 0.8867 | 180 | 0.9311 | 1.0387 | 0.9981 | 0.0588 | 0.7302 | |
|
| 0.9360 | 190 | 0.5445 | 1.0357 | 0.9967 | 0.0565 | 0.7382 | |
|
| 0.9852 | 200 | 0.9154 | 1.0359 | 0.9964 | 0.0556 | 0.7435 | |
|
|
|
|
|
### Environmental Impact |
|
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). |
|
- **Carbon Emitted**: 0.018 kg of CO2 |
|
- **Hours Used**: 0.13 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: 2.7.0.dev0 |
|
- Transformers: 4.39.3 |
|
- PyTorch: 2.1.0+cu121 |
|
- Accelerate: 0.26.1 |
|
- Datasets: 2.18.0 |
|
- Tokenizers: 0.15.2 |
|
|
|
## 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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