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--- |
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language: |
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- en |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- loss:Matryoshka2dLoss |
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- loss:MatryoshkaLoss |
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- loss:CoSENTLoss |
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base_model: distilbert/distilbert-base-uncased |
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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 woman is reading. |
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sentences: |
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- A woman is taking a picture. |
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- Breivik complains of 'ridicule' |
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- The small dog protects its owner. |
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- source_sentence: A man shoots a man. |
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sentences: |
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- A man is shooting off guns. |
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- A tiger walks around aimlessly. |
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- A cat sleeps on purple sheet. |
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- source_sentence: A man is speaking. |
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sentences: |
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- A man is talking. |
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- 19 hurt in New Orleans shooting |
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- The dogs are chasing a black cat. |
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- source_sentence: A man is spitting. |
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sentences: |
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- Breivik complains of 'ridicule' |
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- The man is hiking in the woods. |
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- Eurozone agrees Greece bail-out |
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- source_sentence: A parrot is talking. |
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sentences: |
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- A parrot is talking into a microphone. |
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- A monkey pratices martial arts. |
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- The two men are wearing jeans. |
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pipeline_tag: sentence-similarity |
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co2_eq_emissions: |
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emissions: 5.379215660466108 |
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energy_consumed: 0.013838919430479152 |
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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: 0.072 |
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hardware_used: 1 x NVIDIA GeForce RTX 3090 |
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model-index: |
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- name: SentenceTransformer based on distilbert/distilbert-base-uncased |
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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: 0.861868947947514 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8712617743584893 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.8611484157829896 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.8619125760745536 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.8615299857042606 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.8623855766060573 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.7716399182083511 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.781574012832885 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.861868947947514 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.8712617743584893 |
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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: 0.8281542233533932 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8373087013752897 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.842468233222574 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.8374178427964344 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.8424571958251152 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.8372826604544046 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.6750086731901399 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.656834541089774 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.842468233222574 |
|
name: Pearson Max |
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- type: spearman_max |
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value: 0.8374178427964344 |
|
name: Spearman Max |
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--- |
|
|
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# SentenceTransformer based on distilbert/distilbert-base-uncased |
|
|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 6cdc0aad91f5ae2e6712e91bc7b65d1cf5c05411 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) |
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- **Language:** en |
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<!-- - **License:** Unknown --> |
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|
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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) |
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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel |
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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/distilbert-base-uncased-sts-2d-matryoshka") |
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# Run inference |
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sentences = [ |
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'A parrot is talking.', |
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'A parrot is talking into a microphone.', |
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'A monkey pratices martial arts.', |
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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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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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|
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<!-- |
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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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|
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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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You can finetune this model on your own dataset. |
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|
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<details><summary>Click to expand</summary> |
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|
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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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|
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## Evaluation |
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|
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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) |
|
|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.8619 | |
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| **spearman_cosine** | **0.8713** | |
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| pearson_manhattan | 0.8611 | |
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| spearman_manhattan | 0.8619 | |
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| pearson_euclidean | 0.8615 | |
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| spearman_euclidean | 0.8624 | |
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| pearson_dot | 0.7716 | |
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| spearman_dot | 0.7816 | |
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| pearson_max | 0.8619 | |
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| spearman_max | 0.8713 | |
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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 | 0.8282 | |
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| **spearman_cosine** | **0.8373** | |
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| pearson_manhattan | 0.8425 | |
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| spearman_manhattan | 0.8374 | |
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| pearson_euclidean | 0.8425 | |
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| spearman_euclidean | 0.8373 | |
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| pearson_dot | 0.675 | |
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| spearman_dot | 0.6568 | |
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| pearson_max | 0.8425 | |
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| spearman_max | 0.8374 | |
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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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|
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## Training Details |
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|
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### Training Dataset |
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#### sentence-transformers/stsb |
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* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308) |
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* Size: 5,749 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | score | |
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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 | score | |
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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>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshka2dloss) with these parameters: |
|
```json |
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{ |
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"loss": "CoSENTLoss", |
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"n_layers_per_step": 1, |
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"last_layer_weight": 1.0, |
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"prior_layers_weight": 1.0, |
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"kl_div_weight": 1.0, |
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"kl_temperature": 0.3, |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": 1 |
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} |
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``` |
|
|
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### Evaluation Dataset |
|
|
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#### sentence-transformers/stsb |
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|
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* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308) |
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* Size: 1,500 evaluation samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | sentence1 | sentence2 | score | |
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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 | score | |
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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>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshka2dloss) with these parameters: |
|
```json |
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{ |
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"loss": "CoSENTLoss", |
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"n_layers_per_step": 1, |
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"last_layer_weight": 1.0, |
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"prior_layers_weight": 1.0, |
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"kl_div_weight": 1.0, |
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"kl_temperature": 0.3, |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": 1 |
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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`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `num_train_epochs`: 4 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: False |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
|
- `optim_args`: None |
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- `adafactor`: False |
|
- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: None |
|
- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `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 |
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- `optim_target_modules`: None |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |
|
|:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:| |
|
| 0.2778 | 100 | 7.1781 | 6.6704 | 0.8345 | - | |
|
| 0.5556 | 200 | 6.5316 | 6.7135 | 0.8439 | - | |
|
| 0.8333 | 300 | 6.6267 | 6.8697 | 0.8551 | - | |
|
| 1.1111 | 400 | 6.5709 | 6.7623 | 0.8568 | - | |
|
| 1.3889 | 500 | 6.2898 | 6.4412 | 0.8644 | - | |
|
| 1.6667 | 600 | 6.2021 | 6.7711 | 0.8595 | - | |
|
| 1.9444 | 700 | 6.201 | 6.5252 | 0.8628 | - | |
|
| 2.2222 | 800 | 6.0862 | 6.9795 | 0.8652 | - | |
|
| 2.5 | 900 | 6.303 | 6.7339 | 0.8685 | - | |
|
| 2.7778 | 1000 | 5.9031 | 6.7249 | 0.8694 | - | |
|
| 3.0556 | 1100 | 6.0803 | 6.8350 | 0.8684 | - | |
|
| 3.3333 | 1200 | 6.0564 | 6.9703 | 0.8695 | - | |
|
| 3.6111 | 1300 | 5.8407 | 7.3822 | 0.8707 | - | |
|
| 3.8889 | 1400 | 5.8229 | 7.0442 | 0.8713 | - | |
|
| 4.0 | 1440 | - | - | - | 0.8373 | |
|
|
|
|
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### Environmental Impact |
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Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). |
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- **Energy Consumed**: 0.014 kWh |
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- **Carbon Emitted**: 0.005 kg of CO2 |
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- **Hours Used**: 0.072 hours |
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### Training Hardware |
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- **On Cloud**: No |
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- **GPU Model**: 1 x NVIDIA GeForce RTX 3090 |
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- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K |
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- **RAM Size**: 31.78 GB |
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|
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### Framework Versions |
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- Python: 3.11.6 |
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- Sentence Transformers: 3.0.0.dev0 |
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- Transformers: 4.41.0.dev0 |
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- PyTorch: 2.3.0+cu121 |
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- Accelerate: 0.26.1 |
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- Datasets: 2.18.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### Matryoshka2dLoss |
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```bibtex |
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@misc{li20242d, |
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title={2D Matryoshka Sentence Embeddings}, |
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author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li}, |
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year={2024}, |
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eprint={2402.14776}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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#### MatryoshkaLoss |
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```bibtex |
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@misc{kusupati2024matryoshka, |
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title={Matryoshka Representation Learning}, |
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author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
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year={2024}, |
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eprint={2205.13147}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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#### CoSENTLoss |
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```bibtex |
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@online{kexuefm-8847, |
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title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, |
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author={Su Jianlin}, |
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year={2022}, |
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month={Jan}, |
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url={https://kexue.fm/archives/8847}, |
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} |
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``` |
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