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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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- dense
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- generated_from_trainer
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- dataset_size:222635
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- loss:MatryoshkaLoss
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- loss:MultipleNegativesRankingLoss
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base_model: sentence-transformers/all-MiniLM-L6-v2
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widget:
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- source_sentence: OMBRELLAS
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sentences:
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- '''ALPHASORT, to sort into alphabetic order [v]'''
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- '''OMBRELLA, an umbrella [n]'''
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- '''PHYLLID, the leaf of a liverwort or moss [n]'''
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- source_sentence: ROUNCE
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sentences:
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- '''LYMPHADENITIS, inflammation of the lymph nodes [n]'''
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- '''one who advocates curialism, the system of government of curia [n -S]'''
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- '''part of a hand printing press [n -S]'''
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- source_sentence: SEROON
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sentences:
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- '''(Spanish) a crate or hamper; a bale wrapped in hide, also CEROON, SERON [n
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-S]'''
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- '''a white crystalline soluble phenol used as a photographic developer [n -S]'''
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- '''serving to disseminate [adj]'''
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- source_sentence: BLAFF
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sentences:
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- '''to bark [v -ED, -ING, -S]'''
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- '''RAZORCLAM, a lamellibranch mollusc with a shell like an old-fashioned razor
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handle, also RAZORFISH [n]'''
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- '''HYPERCORRECT, refers to a linguistic construction or pronunciation produced
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by mistaken analogy with standard usage out of a desire to be correct, such as
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"open widely" or "on behalf of my wife and I" [adv]'''
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- source_sentence: TRAUMATOLOGY
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sentences:
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- '''FELLATRIX, a female who fellates [n]'''
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- '''pertaining to a grandparent [adj]'''
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- '''the study of wounds and their effects [n TRAUMATOLOGIES]'''
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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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- cosine_accuracy@1
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- cosine_accuracy@3
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- cosine_accuracy@5
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- cosine_accuracy@10
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- cosine_precision@1
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- cosine_precision@3
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- cosine_precision@5
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- cosine_precision@10
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- cosine_recall@1
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- cosine_recall@3
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- cosine_recall@5
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- cosine_recall@10
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- cosine_ndcg@10
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- cosine_mrr@10
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- cosine_map@100
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model-index:
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- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
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results:
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- task:
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type: information-retrieval
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name: Information Retrieval
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dataset:
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name: dictionary test
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type: dictionary-test
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metrics:
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- type: cosine_accuracy@1
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value: 0.6825254231197672
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name: Cosine Accuracy@1
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- type: cosine_accuracy@3
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value: 0.8121384167594955
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name: Cosine Accuracy@3
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- type: cosine_accuracy@5
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value: 0.831147364260304
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name: Cosine Accuracy@5
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- type: cosine_accuracy@10
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value: 0.850587516619354
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name: Cosine Accuracy@10
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- type: cosine_precision@1
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value: 0.6825254231197672
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name: Cosine Precision@1
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- type: cosine_precision@3
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value: 0.27071280558649846
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name: Cosine Precision@3
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- type: cosine_precision@5
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value: 0.1662294728520608
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name: Cosine Precision@5
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- type: cosine_precision@10
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value: 0.08505875166193541
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name: Cosine Precision@10
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- type: cosine_recall@1
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value: 0.6825254231197672
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name: Cosine Recall@1
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- type: cosine_recall@3
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value: 0.8121384167594955
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name: Cosine Recall@3
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- type: cosine_recall@5
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value: 0.831147364260304
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name: Cosine Recall@5
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- type: cosine_recall@10
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value: 0.850587516619354
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name: Cosine Recall@10
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- type: cosine_ndcg@10
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value: 0.7750717041193917
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name: Cosine Ndcg@10
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- type: cosine_mrr@10
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value: 0.7499954655044675
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name: Cosine Mrr@10
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- type: cosine_map@100
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value: 0.7522443165977887
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name: Cosine Map@100
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---
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# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the csv dataset. 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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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
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- **Maximum Sequence Length:** 256 tokens
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- **Output Dimensionality:** 384 dimensions
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- **Similarity Function:** Cosine Similarity
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- **Training Dataset:**
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- csv
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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/huggingface/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': 256, 'do_lower_case': False, 'architecture': '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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(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("mehularora/scrabble-embed-v2")
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# Run inference
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sentences = [
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'TRAUMATOLOGY',
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"'the study of wounds and their effects [n TRAUMATOLOGIES]'",
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"'FELLATRIX, a female who fellates [n]'",
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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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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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print(similarities)
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# tensor([[1.0000, 0.7472, 0.0801],
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# [0.7472, 1.0000, 0.2525],
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# [0.0801, 0.2525, 1.0000]])
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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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-->
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## Evaluation
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### Metrics
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#### Information Retrieval
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* Dataset: `dictionary-test`
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
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| Metric | Value |
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|:--------------------|:-----------|
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| cosine_accuracy@1 | 0.6825 |
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| cosine_accuracy@3 | 0.8121 |
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| cosine_accuracy@5 | 0.8311 |
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| cosine_accuracy@10 | 0.8506 |
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| cosine_precision@1 | 0.6825 |
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| cosine_precision@3 | 0.2707 |
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| cosine_precision@5 | 0.1662 |
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| cosine_precision@10 | 0.0851 |
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| cosine_recall@1 | 0.6825 |
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| cosine_recall@3 | 0.8121 |
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| cosine_recall@5 | 0.8311 |
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| cosine_recall@10 | 0.8506 |
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| **cosine_ndcg@10** | **0.7751** |
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| cosine_mrr@10 | 0.75 |
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| cosine_map@100 | 0.7522 |
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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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#### csv
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* Dataset: csv
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* Size: 222,635 training samples
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* Columns: <code>word</code> and <code>definition</code>
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* Approximate statistics based on the first 1000 samples:
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| | word | definition |
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|:--------|:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
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| type | string | string |
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| details | <ul><li>min: 3 tokens</li><li>mean: 4.87 tokens</li><li>max: 9 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.32 tokens</li><li>max: 98 tokens</li></ul> |
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* Samples:
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| word | definition |
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|:-------------------------|:------------------------------------------------------------------------------------|
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| <code>LICHGATES</code> | <code>'LICHGATE, the roofed gate of a churchyard, also LYCHGATE [n]'</code> |
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| <code>MOULDING</code> | <code>'a long, narrow strip used to decorate a surface, also MOLDING [n -S]'</code> |
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| <code>PARABAPTISM</code> | <code>'uncanonical baptism [n -S]'</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": "MultipleNegativesRankingLoss",
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"matryoshka_dims": [
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384,
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256
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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`: steps
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- `per_device_train_batch_size`: 64
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- `learning_rate`: 2e-05
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- `num_train_epochs`: 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`: 64
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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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- `torch_empty_cache_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`: 1
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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`: 0.0
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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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- `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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- `parallelism_config`: None
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- `deepspeed`: None
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- `label_smoothing_factor`: 0.0
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- `optim`: adamw_torch_fused
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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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- `project`: huggingface
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- `trackio_space_id`: trackio
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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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- `hub_revision`: None
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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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- `include_tokens_per_second`: False
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- `include_num_input_tokens_seen`: no
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- `neftune_noise_alpha`: None
|
|
|
- `optim_target_modules`: None
|
|
|
- `batch_eval_metrics`: False
|
|
|
- `eval_on_start`: False
|
|
|
- `use_liger_kernel`: False
|
|
|
- `liger_kernel_config`: None
|
|
|
- `eval_use_gather_object`: False
|
|
|
- `average_tokens_across_devices`: True
|
|
|
- `prompts`: None
|
|
|
- `batch_sampler`: batch_sampler
|
|
|
- `multi_dataset_batch_sampler`: proportional
|
|
|
- `router_mapping`: {}
|
|
|
- `learning_rate_mapping`: {}
|
|
|
|
|
|
</details>
|
|
|
|
|
|
### Training Logs
|
|
|
| Epoch | Step | Training Loss | dictionary-test_cosine_ndcg@10 |
|
|
|
|:------:|:----:|:-------------:|:------------------------------:|
|
|
|
| 0.0287 | 100 | 1.0186 | 0.7180 |
|
|
|
| 0.0575 | 200 | 0.7633 | 0.7274 |
|
|
|
| 0.0862 | 300 | 0.75 | 0.7398 |
|
|
|
| 0.1150 | 400 | 0.7503 | 0.7456 |
|
|
|
| 0.1437 | 500 | 0.7271 | 0.7496 |
|
|
|
| 0.1725 | 600 | 0.6531 | 0.7508 |
|
|
|
| 0.2012 | 700 | 0.6586 | 0.7560 |
|
|
|
| 0.2300 | 800 | 0.6559 | 0.7591 |
|
|
|
| 0.2587 | 900 | 0.6116 | 0.7572 |
|
|
|
| 0.2874 | 1000 | 0.615 | 0.7625 |
|
|
|
| 0.3162 | 1100 | 0.5926 | 0.7596 |
|
|
|
| 0.3449 | 1200 | 0.6414 | 0.7623 |
|
|
|
| 0.3737 | 1300 | 0.6143 | 0.7641 |
|
|
|
| 0.4024 | 1400 | 0.6464 | 0.7655 |
|
|
|
| 0.4312 | 1500 | 0.6039 | 0.7676 |
|
|
|
| 0.4599 | 1600 | 0.514 | 0.7643 |
|
|
|
| 0.4886 | 1700 | 0.5719 | 0.7675 |
|
|
|
| 0.5174 | 1800 | 0.612 | 0.7675 |
|
|
|
| 0.5461 | 1900 | 0.5639 | 0.7698 |
|
|
|
| 0.5749 | 2000 | 0.6025 | 0.7672 |
|
|
|
| 0.6036 | 2100 | 0.5623 | 0.7719 |
|
|
|
| 0.6324 | 2200 | 0.5484 | 0.7698 |
|
|
|
| 0.6611 | 2300 | 0.5799 | 0.7730 |
|
|
|
| 0.6899 | 2400 | 0.5253 | 0.7716 |
|
|
|
| 0.7186 | 2500 | 0.5134 | 0.7732 |
|
|
|
| 0.7473 | 2600 | 0.5543 | 0.7721 |
|
|
|
| 0.7761 | 2700 | 0.5342 | 0.7736 |
|
|
|
| 0.8048 | 2800 | 0.5507 | 0.7746 |
|
|
|
| 0.8336 | 2900 | 0.5176 | 0.7737 |
|
|
|
| 0.8623 | 3000 | 0.5067 | 0.7751 |
|
|
|
| 0.8911 | 3100 | 0.548 | 0.7749 |
|
|
|
| 0.9198 | 3200 | 0.5443 | 0.7751 |
|
|
|
| 0.9485 | 3300 | 0.5603 | 0.7751 |
|
|
|
| 0.9773 | 3400 | 0.5774 | 0.7751 |
|
|
|
|
|
|
|
|
|
### Framework Versions
|
|
|
- Python: 3.11.4
|
|
|
- Sentence Transformers: 5.1.2
|
|
|
- Transformers: 4.57.3
|
|
|
- PyTorch: 2.9.1+cpu
|
|
|
- Accelerate: 1.12.0
|
|
|
- Datasets: 4.4.1
|
|
|
- Tokenizers: 0.22.1
|
|
|
|
|
|
## Citation
|
|
|
|
|
|
### BibTeX
|
|
|
|
|
|
#### Sentence Transformers
|
|
|
```bibtex
|
|
|
@inproceedings{reimers-2019-sentence-bert,
|
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
|
|
author = "Reimers, Nils and Gurevych, Iryna",
|
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
|
|
month = "11",
|
|
|
year = "2019",
|
|
|
publisher = "Association for Computational Linguistics",
|
|
|
url = "https://arxiv.org/abs/1908.10084",
|
|
|
}
|
|
|
```
|
|
|
|
|
|
#### MatryoshkaLoss
|
|
|
```bibtex
|
|
|
@misc{kusupati2024matryoshka,
|
|
|
title={Matryoshka Representation Learning},
|
|
|
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},
|
|
|
year={2024},
|
|
|
eprint={2205.13147},
|
|
|
archivePrefix={arXiv},
|
|
|
primaryClass={cs.LG}
|
|
|
}
|
|
|
```
|
|
|
|
|
|
#### MultipleNegativesRankingLoss
|
|
|
```bibtex
|
|
|
@misc{henderson2017efficient,
|
|
|
title={Efficient Natural Language Response Suggestion for Smart Reply},
|
|
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
|
|
|
year={2017},
|
|
|
eprint={1705.00652},
|
|
|
archivePrefix={arXiv},
|
|
|
primaryClass={cs.CL}
|
|
|
}
|
|
|
```
|
|
|
|
|
|
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