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--- |
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language: |
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- id |
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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:6198 |
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- loss:CoSENTLoss |
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base_model: intfloat/multilingual-e5-base |
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datasets: |
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- Pustekhan-ITB/stsb-indo-edu |
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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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- pearson_cosine |
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- spearman_cosine |
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model-index: |
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- name: SentenceTransformer based on intfloat/multilingual-e5-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: stsb indo edu dev |
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type: stsb-indo-edu-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.1930033858243812 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.17647076252403324 |
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name: Spearman Cosine |
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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: stsb indo edu test |
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type: stsb-indo-edu-test |
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metrics: |
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- type: pearson_cosine |
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value: 0.15065000397563194 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.1512326380689479 |
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name: Spearman Cosine |
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--- |
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# SentenceTransformer based on intfloat/multilingual-e5-base |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on the [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision 835193815a3936a24a0ee7dc9e3d48c1fbb19c55 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) |
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- **Language:** id |
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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: XLMRobertaModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("ewideplus/indoedu-e5-base") |
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# Run inference |
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sentences = [ |
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'The weather is lovely today.', |
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"It's so sunny outside!", |
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'He drove to the stadium.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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<!-- |
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### 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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### 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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#### Semantic Similarity |
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* Datasets: `stsb-indo-edu-dev` and `stsb-indo-edu-test` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | stsb-indo-edu-dev | stsb-indo-edu-test | |
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|:--------------------|:------------------|:-------------------| |
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| pearson_cosine | 0.193 | 0.1507 | |
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| **spearman_cosine** | **0.1765** | **0.1512** | |
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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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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Dataset |
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#### stsb-indo-edu |
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* Dataset: [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) at [f84d4d6](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu/tree/f84d4d6eaca768507bd0f298aef6f3f1a98ddefc) |
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* Size: 6,198 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 | list | list | float | |
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| details | <ul><li>min: 18 elements</li><li>mean: 58.40 elements</li><li>max: 137 elements</li></ul> | <ul><li>min: 15 elements</li><li>mean: 54.31 elements</li><li>max: 118 elements</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.46</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>['query: P', 'query: e', 'query: l', 'query: a', 'query: j', ...]</code> | <code>['passage: T', 'passage: a', 'passage: r', 'passage: i', 'passage: a', ...]</code> | <code>0.76</code> | |
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| <code>['query: S', 'query: e', 'query: b', 'query: e', 'query: l', ...]</code> | <code>['passage: U', 'passage: p', 'passage: a', 'passage: y', 'passage: a', ...]</code> | <code>0.85</code> | |
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| <code>['query: B', 'query: e', 'query: b', 'query: e', 'query: r', ...]</code> | <code>['passage: I', 'passage: n', 'passage: i', 'passage: ', 'passage: m', ...]</code> | <code>0.63</code> | |
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "pairwise_cos_sim" |
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} |
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``` |
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### Evaluation Dataset |
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#### stsb-indo-edu |
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* Dataset: [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) at [f84d4d6](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu/tree/f84d4d6eaca768507bd0f298aef6f3f1a98ddefc) |
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* Size: 1,536 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: |
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| | sentence1 | sentence2 | score | |
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|:--------|:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | list | list | float | |
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| details | <ul><li>min: 14 elements</li><li>mean: 86.67 elements</li><li>max: 172 elements</li></ul> | <ul><li>min: 22 elements</li><li>mean: 88.94 elements</li><li>max: 177 elements</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</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>['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...]</code> | <code>['passage: S', 'passage: e', 'passage: o', 'passage: r', 'passage: a', ...]</code> | <code>1.0</code> | |
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| <code>['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...]</code> | <code>['passage: S', 'passage: e', 'passage: o', 'passage: r', 'passage: a', ...]</code> | <code>0.95</code> | |
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| <code>['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...]</code> | <code>['passage: P', 'passage: r', 'passage: i', 'passage: a', 'passage: ', ...]</code> | <code>1.0</code> | |
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "pairwise_cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `learning_rate`: 1e-05 |
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- `weight_decay`: 0.01 |
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- `num_train_epochs`: 5 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 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`: None |
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- `learning_rate`: 1e-05 |
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- `weight_decay`: 0.01 |
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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`: 5 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: 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`: batch_sampler |
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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 | stsb-indo-edu-dev_spearman_cosine | stsb-indo-edu-test_spearman_cosine | |
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|:------:|:----:|:-------------:|:---------------:|:---------------------------------:|:----------------------------------:| |
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| -1 | -1 | - | - | 0.0995 | - | |
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| 0.5155 | 100 | 6.2244 | 4.7594 | 0.1027 | - | |
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| 1.0309 | 200 | 6.1605 | 4.7518 | 0.1502 | - | |
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| 1.5464 | 300 | 6.16 | 4.7553 | 0.1564 | - | |
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| 2.0619 | 400 | 6.1609 | 4.7527 | 0.1714 | - | |
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| 2.5773 | 500 | 6.1593 | 4.7698 | 0.1495 | - | |
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| 3.0928 | 600 | 6.1517 | 4.7516 | 0.1657 | - | |
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| 3.6082 | 700 | 6.1555 | 4.7463 | 0.1787 | - | |
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| 4.1237 | 800 | 6.1452 | 4.7548 | 0.1665 | - | |
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| 4.6392 | 900 | 6.1523 | 4.7494 | 0.1765 | - | |
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| -1 | -1 | - | - | - | 0.1512 | |
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### Framework Versions |
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- Python: 3.11.11 |
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- Sentence Transformers: 3.4.1 |
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- Transformers: 4.48.3 |
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- PyTorch: 2.5.1+cu124 |
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- Accelerate: 1.3.0 |
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- Datasets: 3.3.2 |
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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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#### 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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