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--- |
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language: [] |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:1267 |
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- loss:CoSENTLoss |
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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datasets: [] |
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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: Give me suggestions for a high-quality DSLR camera |
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sentences: |
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- faq query |
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- subscription query |
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- faq query |
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- source_sentence: Aidez-moi à configurer une nouvelle adresse e-mail |
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sentences: |
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- order query |
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- faq query |
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- feedback query |
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- source_sentence: Как я могу изменить адрес доставки? |
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sentences: |
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- support query |
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- product query |
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- product query |
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- source_sentence: ساعدني في حذف الملفات الغير مرغوب فيها من هاتفي |
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sentences: |
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- technical support query |
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- product recommendation |
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- faq query |
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- source_sentence: Envoyez-moi la politique de garantie de ce produit |
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sentences: |
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- faq query |
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- account query |
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- faq query |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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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: MiniLM dev |
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type: MiniLM-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.6538226572138826 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.6336766646599241 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.5799895241429639 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.5525776786782183 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.5732001104236694 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.5394971970682657 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.6359725423136287 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.6237936341101822 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.6538226572138826 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.6336766646599241 |
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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: MiniLM test |
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type: MiniLM-test |
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metrics: |
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- type: pearson_cosine |
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value: 0.6682368113711722 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.6222011918428743 |
|
name: Spearman Cosine |
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- type: pearson_manhattan |
|
value: 0.5714617063306076 |
|
name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.5481366191719228 |
|
name: Spearman Manhattan |
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- type: pearson_euclidean |
|
value: 0.5726946277850402 |
|
name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.549312247309557 |
|
name: Spearman Euclidean |
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- type: pearson_dot |
|
value: 0.6396412507506479 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.6107388175009413 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.6682368113711722 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.6222011918428743 |
|
name: Spearman Max |
|
--- |
|
|
|
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 384 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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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': 128, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 384, '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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### Direct Usage (Sentence Transformers) |
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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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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("philipp-zettl/MiniLM-similarity-small") |
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# Run inference |
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sentences = [ |
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'Envoyez-moi la politique de garantie de ce produit', |
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'faq query', |
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'account query', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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|
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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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<!-- |
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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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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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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: `MiniLM-dev` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.6538 | |
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| **spearman_cosine** | **0.6337** | |
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| pearson_manhattan | 0.58 | |
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| spearman_manhattan | 0.5526 | |
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| pearson_euclidean | 0.5732 | |
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| spearman_euclidean | 0.5395 | |
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| pearson_dot | 0.636 | |
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| spearman_dot | 0.6238 | |
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| pearson_max | 0.6538 | |
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| spearman_max | 0.6337 | |
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|
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#### Semantic Similarity |
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* Dataset: `MiniLM-test` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.6682 | |
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| **spearman_cosine** | **0.6222** | |
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| pearson_manhattan | 0.5715 | |
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| spearman_manhattan | 0.5481 | |
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| pearson_euclidean | 0.5727 | |
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| spearman_euclidean | 0.5493 | |
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| pearson_dot | 0.6396 | |
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| spearman_dot | 0.6107 | |
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| pearson_max | 0.6682 | |
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| spearman_max | 0.6222 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 1,267 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: 10.77 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 5.31 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.67</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>Get information on the next art exhibition</code> | <code>product query</code> | <code>0.0</code> | |
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| <code>Show me how to update my profile</code> | <code>product query</code> | <code>0.0</code> | |
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| <code>Покажите мне доступные варианты полетов в Турцию</code> | <code>faq query</code> | <code>0.0</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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|
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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 159 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 | string | string | float | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 10.65 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 5.35 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.67</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>Sende mir die Bestellbestätigung per E-Mail</code> | <code>order query</code> | <code>0.0</code> | |
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| <code>How do I add a new payment method?</code> | <code>faq query</code> | <code>1.0</code> | |
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| <code>No puedo conectar mi impresora, ¿puedes ayudarme?</code> | <code>support query</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: |
|
```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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|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: steps |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 2 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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|
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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`: 8 |
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- `per_device_eval_batch_size`: 8 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 2 |
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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`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
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- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
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| Epoch | Step | Training Loss | loss | MiniLM-dev_spearman_cosine | MiniLM-test_spearman_cosine | |
|
|:------:|:----:|:-------------:|:------:|:--------------------------:|:---------------------------:| |
|
| 0.0629 | 10 | 6.2479 | 2.5890 | 0.1448 | - | |
|
| 0.1258 | 20 | 4.3549 | 2.2787 | 0.1965 | - | |
|
| 0.1887 | 30 | 3.5969 | 2.0104 | 0.2599 | - | |
|
| 0.2516 | 40 | 2.4979 | 1.7269 | 0.3357 | - | |
|
| 0.3145 | 50 | 2.5551 | 1.5747 | 0.4439 | - | |
|
| 0.3774 | 60 | 3.1446 | 1.4892 | 0.4750 | - | |
|
| 0.4403 | 70 | 2.1353 | 1.5305 | 0.4662 | - | |
|
| 0.5031 | 80 | 2.9341 | 1.3718 | 0.4848 | - | |
|
| 0.5660 | 90 | 2.8709 | 1.2469 | 0.5316 | - | |
|
| 0.6289 | 100 | 2.1367 | 1.2558 | 0.5436 | - | |
|
| 0.6918 | 110 | 2.2735 | 1.2939 | 0.5392 | - | |
|
| 0.7547 | 120 | 2.8646 | 1.1206 | 0.5616 | - | |
|
| 0.8176 | 130 | 3.3204 | 1.0213 | 0.5662 | - | |
|
| 0.8805 | 140 | 0.8989 | 0.9866 | 0.5738 | - | |
|
| 0.9434 | 150 | 0.0057 | 0.9961 | 0.5674 | - | |
|
| 1.0063 | 160 | 0.0019 | 1.0111 | 0.5674 | - | |
|
| 1.0692 | 170 | 0.4617 | 1.0275 | 0.5747 | - | |
|
| 1.1321 | 180 | 0.0083 | 1.0746 | 0.5732 | - | |
|
| 1.1950 | 190 | 0.5048 | 1.0968 | 0.5753 | - | |
|
| 1.2579 | 200 | 0.0002 | 1.0840 | 0.5738 | - | |
|
| 1.3208 | 210 | 0.07 | 1.0364 | 0.5753 | - | |
|
| 1.3836 | 220 | 0.0 | 0.9952 | 0.5750 | - | |
|
| 1.4465 | 230 | 0.0 | 0.9922 | 0.5744 | - | |
|
| 1.5094 | 240 | 0.0 | 0.9923 | 0.5726 | - | |
|
| 1.0126 | 250 | 0.229 | 0.9930 | 0.5729 | - | |
|
| 1.0755 | 260 | 2.2061 | 0.9435 | 0.5880 | - | |
|
| 1.1384 | 270 | 2.7711 | 0.8892 | 0.6078 | - | |
|
| 1.2013 | 280 | 0.7528 | 0.8886 | 0.6148 | - | |
|
| 1.2642 | 290 | 0.386 | 0.8927 | 0.6162 | - | |
|
| 1.3270 | 300 | 0.8902 | 0.8710 | 0.6267 | - | |
|
| 1.3899 | 310 | 0.9534 | 0.8429 | 0.6337 | - | |
|
| 1.4403 | 318 | - | - | - | 0.6222 | |
|
|
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|
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### Framework Versions |
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- Python: 3.10.14 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.3.1+cu121 |
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- Accelerate: 0.33.0 |
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- Datasets: 2.21.0 |
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- Tokenizers: 0.19.1 |
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|
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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", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
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#### 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}, |
|
year={2022}, |
|
month={Jan}, |
|
url={https://kexue.fm/archives/8847}, |
|
} |
|
``` |
|
|
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