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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:40906 |
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- loss:MatryoshkaLoss |
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- loss:MegaBatchMarginLoss |
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widget: |
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- source_sentence: >- |
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One of three laminate structures that form the spindle pole body; the inner |
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plaque is in the nucleus. |
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sentences: |
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- >- |
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maturation of SSU-rRNA from tetracistronic rRNA transcript (SSU-rRNA, 5.8S |
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rRNA, 2S rRNA, LSU-rRNA) |
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- leukotriene receptor activity |
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- inner plaque of spindle pole body |
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- source_sentence: >- |
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The covalent attachment of a myristoyl group to the N-terminal amino acid |
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residue of a protein. |
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sentences: |
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- MHC class I protein complex assembly |
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- N-terminal protein myristoylation |
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- neurotrophin receptor activity |
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- source_sentence: >- |
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The inner, i.e. lumen-facing, lipid bilayer of the plastid envelope; also |
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faces the plastid stroma. |
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sentences: |
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- plastid inner membrane |
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- neuron migration involved in retrograde extension |
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- stomatal complex morphogenesis |
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- source_sentence: >- |
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Initiation of a region of tissue in a plant that is composed of one or more |
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undifferentiated cells capable of undergoing mitosis and differentiation, |
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thereby effecting growth and development of a plant by giving rise to more |
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meristem or specialized tissue. |
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sentences: |
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- meristem initiation |
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- polytene chromosome |
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- cardiac ventricle development |
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- source_sentence: >- |
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The sex chromosome present in both sexes of species in which the male is the |
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heterogametic sex. Two copies of the X chromosome are present in each |
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somatic cell of females and one copy is present in males. |
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sentences: |
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- establishment of cell polarity involved in gastrulation cell migration |
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- X chromosome |
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- somatic diversification of immune receptors by N region addition |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- src2trg_accuracy |
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- trg2src_accuracy |
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- mean_accuracy |
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model-index: |
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- name: SentenceTransformer |
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results: |
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- task: |
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type: translation |
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name: Translation |
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dataset: |
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name: Unknown |
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type: unknown |
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metrics: |
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- type: src2trg_accuracy |
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value: 0.7840546697038724 |
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name: Src2Trg Accuracy |
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- type: trg2src_accuracy |
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value: 0.7757023538344723 |
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name: Trg2Src Accuracy |
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- type: mean_accuracy |
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value: 0.7798785117691723 |
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name: Mean Accuracy |
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license: mit |
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language: |
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- en |
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base_model: |
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- Snowflake/snowflake-arctic-embed-m-v1.5 |
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datasets: |
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- NothingMuch/GO-Terms |
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--- |
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# SentenceTransformer |
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This is a [sentence-transformers](https://www.SBERT.net) model trained on the parquet dataset. It maps sentences & paragraphs to a 128-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:** [Unknown](https://huggingface.co/unknown) --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 128 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- parquet |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("NothingMuch/GO-Term-Embeddings") |
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# Run inference |
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sentences = [ |
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'The sex chromosome present in both sexes of species in which the male is the heterogametic sex. Two copies of the X chromosome are present in each somatic cell of females and one copy is present in males.', |
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'X chromosome', |
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'somatic diversification of immune receptors by N region addition', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 128] |
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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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## Evaluation |
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### Metrics |
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#### Translation |
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* Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator) |
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| Metric | Value | |
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|:------------------|:-----------| |
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| src2trg_accuracy | 0.7841 | |
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| trg2src_accuracy | 0.7757 | |
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| **mean_accuracy** | **0.7799** | |
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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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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### parquet |
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* Dataset: parquet |
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* Size: 40,906 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 8 tokens</li><li>mean: 42.05 tokens</li><li>max: 192 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.48 tokens</li><li>max: 40 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------| |
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| <code>Catalysis of the transfer of a mannose residue to an oligosaccharide, forming an alpha-(1->6) linkage.</code> | <code>1,6-alpha-mannosyltransferase activity</code> | |
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| <code>Catalysis of the hydrolysis of ester linkages within a single-stranded deoxyribonucleic acid molecule by creating internal breaks.</code> | <code>single-stranded DNA specific endodeoxyribonuclease activity</code> | |
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| <code>Catalysis of the hydrolysis of ester linkages within a single-stranded deoxyribonucleic acid molecule by creating internal breaks.</code> | <code>ssDNA-specific endodeoxyribonuclease activity</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MegaBatchMarginLoss", |
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"matryoshka_dims": [ |
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64, |
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32 |
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], |
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"matryoshka_weights": [ |
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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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#### parquet |
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* Dataset: parquet |
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* Size: 6,585 evaluation samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 8 tokens</li><li>mean: 41.29 tokens</li><li>max: 253 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.23 tokens</li><li>max: 44 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------| |
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| <code>The maintenance of the structure and integrity of the mitochondrial genome; includes replication and segregation of the mitochondrial chromosome.</code> | <code>mitochondrial genome maintenance</code> | |
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| <code>The repair of single strand breaks in DNA. Repair of such breaks is mediated by the same enzyme systems as are used in base excision repair.</code> | <code>single strand break repair</code> | |
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| <code>Any process that modulates the frequency, rate or extent of DNA recombination, a DNA metabolic process in which a new genotype is formed by reassortment of genes resulting in gene combinations different from those that were present in the parents.</code> | <code>regulation of DNA recombination</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MegaBatchMarginLoss", |
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"matryoshka_dims": [ |
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64, |
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32 |
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], |
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"matryoshka_weights": [ |
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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`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `torch_empty_cache_steps`: 250 |
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- `learning_rate`: 0.00025 |
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- `lr_scheduler_type`: cosine_with_restarts |
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- `warmup_steps`: 25 |
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- `seed`: 25 |
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- `load_best_model_at_end`: True |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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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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- `torch_empty_cache_steps`: 250 |
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- `learning_rate`: 0.00025 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 3 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine_with_restarts |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 25 |
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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`: 25 |
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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`: 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`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | Validation Loss | mean_accuracy | |
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|:----------:|:--------:|:-------------:|:---------------:|:-------------:| |
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| 1.0016 | 641 | 0.2501 | 0.6276 | 0.7343 | |
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| 2.0016 | 1282 | 0.3146 | 0.5520 | 0.7651 | |
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| **2.9969** | **1920** | **0.1976** | **0.5097** | **0.7799** | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.14 |
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- Sentence Transformers: 3.3.1 |
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- Transformers: 4.47.0 |
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- PyTorch: 2.4.0 |
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- Accelerate: 1.2.0 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.21.0 |
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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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#### 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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#### MegaBatchMarginLoss |
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```bibtex |
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@inproceedings{wieting-gimpel-2018-paranmt, |
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title = "{P}ara{NMT}-50{M}: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations", |
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author = "Wieting, John and Gimpel, Kevin", |
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editor = "Gurevych, Iryna and Miyao, Yusuke", |
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booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", |
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month = jul, |
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year = "2018", |
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address = "Melbourne, Australia", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/P18-1042", |
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doi = "10.18653/v1/P18-1042", |
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pages = "451--462", |
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} |
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``` |
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