| | --- |
| | base_model: intfloat/multilingual-e5-large |
| | library_name: sentence-transformers |
| | metrics: |
| | - cosine_accuracy@1 |
| | - cosine_accuracy@3 |
| | - cosine_accuracy@5 |
| | - cosine_accuracy@10 |
| | - cosine_precision@1 |
| | - cosine_precision@3 |
| | - cosine_precision@5 |
| | - cosine_precision@10 |
| | - cosine_recall@1 |
| | - cosine_recall@3 |
| | - cosine_recall@5 |
| | - cosine_recall@10 |
| | - cosine_ndcg@10 |
| | - cosine_mrr@10 |
| | - cosine_map@100 |
| | pipeline_tag: sentence-similarity |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:198 |
| | - loss:MatryoshkaLoss |
| | - loss:MultipleNegativesRankingLoss |
| | widget: |
| | - source_sentence: Najčešći tipovi uključuju iznad/ispod 2.5, ukupno golova, i klađenje |
| | na broj golova u poluvremenima. |
| | sentences: |
| | - Koji su najčešći tipovi klađenja na golove? |
| | - Koje kladionice u Srbiji nude DNB opciju? |
| | - Šta je hendikep klađenje? |
| | - source_sentence: Facebook grupe posvećene klađenju omogućavaju korisnicima da dobijaju |
| | savete i predloge od velikih zajednica korisnika i kladioničara. |
| | sentences: |
| | - Šta je limit u klađenju? |
| | - Kako se koristi Facebook za klađenje? |
| | - Šta je cash-out opcija u uživo klađenju? |
| | - source_sentence: Najčešći tipovi uključuju klađenje na konačan ishod, broj gemova, |
| | broj setova, i klađenje uživo. |
| | sentences: |
| | - Koje su prednosti praćenja utakmica uživo? |
| | - Koji su najčešći tipovi klađenja na tenis? |
| | - Šta je e-novčanik? |
| | - source_sentence: Premijum provizija je dodatna naknada koju berze kvota mogu naplatiti |
| | igračima za specifične usluge ili dobitke. |
| | sentences: |
| | - Šta je premijum provizija? |
| | - Koje su strategije za uspešno uživo klađenje? |
| | - Kako funkcioniše klađenje na ukupan broj poena timova? |
| | - source_sentence: '''Super Jenki'' sistem uključuje pet događaja i 26 pojedinačnih |
| | opklada, takođe poznat kao kanadski sistem.' |
| | sentences: |
| | - Šta je 'Super Jenki' sistem klađenja? |
| | - Šta je procena verovatnoće? |
| | - Kako klađenje uživo funkcioniše u tenisu? |
| | model-index: |
| | - name: SentenceTransformer based on intfloat/multilingual-e5-large |
| | results: |
| | - task: |
| | type: information-retrieval |
| | name: Information Retrieval |
| | dataset: |
| | name: dim 768 |
| | type: dim_768 |
| | metrics: |
| | - type: cosine_accuracy@1 |
| | value: 0.8260869565217391 |
| | name: Cosine Accuracy@1 |
| | - type: cosine_accuracy@3 |
| | value: 0.9565217391304348 |
| | name: Cosine Accuracy@3 |
| | - type: cosine_accuracy@5 |
| | value: 1.0 |
| | name: Cosine Accuracy@5 |
| | - type: cosine_accuracy@10 |
| | value: 1.0 |
| | name: Cosine Accuracy@10 |
| | - type: cosine_precision@1 |
| | value: 0.8260869565217391 |
| | name: Cosine Precision@1 |
| | - type: cosine_precision@3 |
| | value: 0.31884057971014484 |
| | name: Cosine Precision@3 |
| | - type: cosine_precision@5 |
| | value: 0.20000000000000007 |
| | name: Cosine Precision@5 |
| | - type: cosine_precision@10 |
| | value: 0.10000000000000003 |
| | name: Cosine Precision@10 |
| | - type: cosine_recall@1 |
| | value: 0.8260869565217391 |
| | name: Cosine Recall@1 |
| | - type: cosine_recall@3 |
| | value: 0.9565217391304348 |
| | name: Cosine Recall@3 |
| | - type: cosine_recall@5 |
| | value: 1.0 |
| | name: Cosine Recall@5 |
| | - type: cosine_recall@10 |
| | value: 1.0 |
| | name: Cosine Recall@10 |
| | - type: cosine_ndcg@10 |
| | value: 0.9271072095125116 |
| | name: Cosine Ndcg@10 |
| | - type: cosine_mrr@10 |
| | value: 0.9021739130434783 |
| | name: Cosine Mrr@10 |
| | - type: cosine_map@100 |
| | value: 0.9021739130434783 |
| | name: Cosine Map@100 |
| | - task: |
| | type: information-retrieval |
| | name: Information Retrieval |
| | dataset: |
| | name: dim 512 |
| | type: dim_512 |
| | metrics: |
| | - type: cosine_accuracy@1 |
| | value: 0.8695652173913043 |
| | name: Cosine Accuracy@1 |
| | - type: cosine_accuracy@3 |
| | value: 1.0 |
| | name: Cosine Accuracy@3 |
| | - type: cosine_accuracy@5 |
| | value: 1.0 |
| | name: Cosine Accuracy@5 |
| | - type: cosine_accuracy@10 |
| | value: 1.0 |
| | name: Cosine Accuracy@10 |
| | - type: cosine_precision@1 |
| | value: 0.8695652173913043 |
| | name: Cosine Precision@1 |
| | - type: cosine_precision@3 |
| | value: 0.3333333333333332 |
| | name: Cosine Precision@3 |
| | - type: cosine_precision@5 |
| | value: 0.20000000000000007 |
| | name: Cosine Precision@5 |
| | - type: cosine_precision@10 |
| | value: 0.10000000000000003 |
| | name: Cosine Precision@10 |
| | - type: cosine_recall@1 |
| | value: 0.8695652173913043 |
| | name: Cosine Recall@1 |
| | - type: cosine_recall@3 |
| | value: 1.0 |
| | name: Cosine Recall@3 |
| | - type: cosine_recall@5 |
| | value: 1.0 |
| | name: Cosine Recall@5 |
| | - type: cosine_recall@10 |
| | value: 1.0 |
| | name: Cosine Recall@10 |
| | - type: cosine_ndcg@10 |
| | value: 0.9461678046583877 |
| | name: Cosine Ndcg@10 |
| | - type: cosine_mrr@10 |
| | value: 0.9275362318840579 |
| | name: Cosine Mrr@10 |
| | - type: cosine_map@100 |
| | value: 0.9275362318840579 |
| | name: Cosine Map@100 |
| | - task: |
| | type: information-retrieval |
| | name: Information Retrieval |
| | dataset: |
| | name: dim 256 |
| | type: dim_256 |
| | metrics: |
| | - type: cosine_accuracy@1 |
| | value: 0.8260869565217391 |
| | name: Cosine Accuracy@1 |
| | - type: cosine_accuracy@3 |
| | value: 1.0 |
| | name: Cosine Accuracy@3 |
| | - type: cosine_accuracy@5 |
| | value: 1.0 |
| | name: Cosine Accuracy@5 |
| | - type: cosine_accuracy@10 |
| | value: 1.0 |
| | name: Cosine Accuracy@10 |
| | - type: cosine_precision@1 |
| | value: 0.8260869565217391 |
| | name: Cosine Precision@1 |
| | - type: cosine_precision@3 |
| | value: 0.3333333333333332 |
| | name: Cosine Precision@3 |
| | - type: cosine_precision@5 |
| | value: 0.20000000000000007 |
| | name: Cosine Precision@5 |
| | - type: cosine_precision@10 |
| | value: 0.10000000000000003 |
| | name: Cosine Precision@10 |
| | - type: cosine_recall@1 |
| | value: 0.8260869565217391 |
| | name: Cosine Recall@1 |
| | - type: cosine_recall@3 |
| | value: 1.0 |
| | name: Cosine Recall@3 |
| | - type: cosine_recall@5 |
| | value: 1.0 |
| | name: Cosine Recall@5 |
| | - type: cosine_recall@10 |
| | value: 1.0 |
| | name: Cosine Recall@10 |
| | - type: cosine_ndcg@10 |
| | value: 0.9301212722049728 |
| | name: Cosine Ndcg@10 |
| | - type: cosine_mrr@10 |
| | value: 0.9057971014492753 |
| | name: Cosine Mrr@10 |
| | - type: cosine_map@100 |
| | value: 0.9057971014492753 |
| | name: Cosine Map@100 |
| | - task: |
| | type: information-retrieval |
| | name: Information Retrieval |
| | dataset: |
| | name: dim 128 |
| | type: dim_128 |
| | metrics: |
| | - type: cosine_accuracy@1 |
| | value: 0.782608695652174 |
| | name: Cosine Accuracy@1 |
| | - type: cosine_accuracy@3 |
| | value: 0.9565217391304348 |
| | name: Cosine Accuracy@3 |
| | - type: cosine_accuracy@5 |
| | value: 1.0 |
| | name: Cosine Accuracy@5 |
| | - type: cosine_accuracy@10 |
| | value: 1.0 |
| | name: Cosine Accuracy@10 |
| | - type: cosine_precision@1 |
| | value: 0.782608695652174 |
| | name: Cosine Precision@1 |
| | - type: cosine_precision@3 |
| | value: 0.31884057971014484 |
| | name: Cosine Precision@3 |
| | - type: cosine_precision@5 |
| | value: 0.20000000000000007 |
| | name: Cosine Precision@5 |
| | - type: cosine_precision@10 |
| | value: 0.10000000000000003 |
| | name: Cosine Precision@10 |
| | - type: cosine_recall@1 |
| | value: 0.782608695652174 |
| | name: Cosine Recall@1 |
| | - type: cosine_recall@3 |
| | value: 0.9565217391304348 |
| | name: Cosine Recall@3 |
| | - type: cosine_recall@5 |
| | value: 1.0 |
| | name: Cosine Recall@5 |
| | - type: cosine_recall@10 |
| | value: 1.0 |
| | name: Cosine Recall@10 |
| | - type: cosine_ndcg@10 |
| | value: 0.9091552965878422 |
| | name: Cosine Ndcg@10 |
| | - type: cosine_mrr@10 |
| | value: 0.8782608695652173 |
| | name: Cosine Mrr@10 |
| | - type: cosine_map@100 |
| | value: 0.8782608695652173 |
| | name: Cosine Map@100 |
| | - task: |
| | type: information-retrieval |
| | name: Information Retrieval |
| | dataset: |
| | name: dim 64 |
| | type: dim_64 |
| | metrics: |
| | - type: cosine_accuracy@1 |
| | value: 0.8260869565217391 |
| | name: Cosine Accuracy@1 |
| | - type: cosine_accuracy@3 |
| | value: 0.9565217391304348 |
| | name: Cosine Accuracy@3 |
| | - type: cosine_accuracy@5 |
| | value: 0.9565217391304348 |
| | name: Cosine Accuracy@5 |
| | - type: cosine_accuracy@10 |
| | value: 1.0 |
| | name: Cosine Accuracy@10 |
| | - type: cosine_precision@1 |
| | value: 0.8260869565217391 |
| | name: Cosine Precision@1 |
| | - type: cosine_precision@3 |
| | value: 0.31884057971014484 |
| | name: Cosine Precision@3 |
| | - type: cosine_precision@5 |
| | value: 0.19130434782608702 |
| | name: Cosine Precision@5 |
| | - type: cosine_precision@10 |
| | value: 0.10000000000000003 |
| | name: Cosine Precision@10 |
| | - type: cosine_recall@1 |
| | value: 0.8260869565217391 |
| | name: Cosine Recall@1 |
| | - type: cosine_recall@3 |
| | value: 0.9565217391304348 |
| | name: Cosine Recall@3 |
| | - type: cosine_recall@5 |
| | value: 0.9565217391304348 |
| | name: Cosine Recall@5 |
| | - type: cosine_recall@10 |
| | value: 1.0 |
| | name: Cosine Recall@10 |
| | - type: cosine_ndcg@10 |
| | value: 0.9164054079968976 |
| | name: Cosine Ndcg@10 |
| | - type: cosine_mrr@10 |
| | value: 0.8894927536231884 |
| | name: Cosine Mrr@10 |
| | - type: cosine_map@100 |
| | value: 0.8894927536231884 |
| | name: Cosine Map@100 |
| | --- |
| | |
| | # SentenceTransformer based on intfloat/multilingual-e5-large |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
| |
|
| | ## Model Details |
| |
|
| | ### Model Description |
| | - **Model Type:** Sentence Transformer |
| | - **Base model:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) <!-- at revision ab10c1a7f42e74530fe7ae5be82e6d4f11a719eb --> |
| | - **Maximum Sequence Length:** 512 tokens |
| | - **Output Dimensionality:** 1024 tokens |
| | - **Similarity Function:** Cosine Similarity |
| | - **Training Dataset:** |
| | - json |
| | <!-- - **Language:** Unknown --> |
| | <!-- - **License:** Unknown --> |
| |
|
| | ### Model Sources |
| |
|
| | - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| | - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| | - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
| |
|
| | ### Full Model Architecture |
| |
|
| | ``` |
| | SentenceTransformer( |
| | (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
| | (1): Pooling({'word_embedding_dimension': 1024, '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}) |
| | (2): Normalize() |
| | ) |
| | ``` |
| |
|
| | ## Usage |
| |
|
| | ### Direct Usage (Sentence Transformers) |
| |
|
| | First install the Sentence Transformers library: |
| |
|
| | ```bash |
| | pip install -U sentence-transformers |
| | ``` |
| |
|
| | Then you can load this model and run inference. |
| | ```python |
| | from sentence_transformers import SentenceTransformer |
| | |
| | # Download from the 🤗 Hub |
| | model = SentenceTransformer("luka023/proba") |
| | # Run inference |
| | sentences = [ |
| | "'Super Jenki' sistem uključuje pet događaja i 26 pojedinačnih opklada, takođe poznat kao kanadski sistem.", |
| | "Šta je 'Super Jenki' sistem klađenja?", |
| | 'Kako klađenje uživo funkcioniše u tenisu?', |
| | ] |
| | embeddings = model.encode(sentences) |
| | print(embeddings.shape) |
| | # [3, 1024] |
| | |
| | # Get the similarity scores for the embeddings |
| | similarities = model.similarity(embeddings, embeddings) |
| | print(similarities.shape) |
| | # [3, 3] |
| | ``` |
| |
|
| | <!-- |
| | ### Direct Usage (Transformers) |
| |
|
| | <details><summary>Click to see the direct usage in Transformers</summary> |
| |
|
| | </details> |
| | --> |
| |
|
| | <!-- |
| | ### Downstream Usage (Sentence Transformers) |
| |
|
| | You can finetune this model on your own dataset. |
| |
|
| | <details><summary>Click to expand</summary> |
| |
|
| | </details> |
| | --> |
| |
|
| | <!-- |
| | ### Out-of-Scope Use |
| |
|
| | *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| | --> |
| |
|
| | ## Evaluation |
| |
|
| | ### Metrics |
| |
|
| | #### Information Retrieval |
| | * Dataset: `dim_768` |
| | * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
| |
|
| | | Metric | Value | |
| | |:--------------------|:-----------| |
| | | cosine_accuracy@1 | 0.8261 | |
| | | cosine_accuracy@3 | 0.9565 | |
| | | cosine_accuracy@5 | 1.0 | |
| | | cosine_accuracy@10 | 1.0 | |
| | | cosine_precision@1 | 0.8261 | |
| | | cosine_precision@3 | 0.3188 | |
| | | cosine_precision@5 | 0.2 | |
| | | cosine_precision@10 | 0.1 | |
| | | cosine_recall@1 | 0.8261 | |
| | | cosine_recall@3 | 0.9565 | |
| | | cosine_recall@5 | 1.0 | |
| | | cosine_recall@10 | 1.0 | |
| | | cosine_ndcg@10 | 0.9271 | |
| | | cosine_mrr@10 | 0.9022 | |
| | | **cosine_map@100** | **0.9022** | |
| | |
| | #### Information Retrieval |
| | * Dataset: `dim_512` |
| | * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
| | |
| | | Metric | Value | |
| | |:--------------------|:-----------| |
| | | cosine_accuracy@1 | 0.8696 | |
| | | cosine_accuracy@3 | 1.0 | |
| | | cosine_accuracy@5 | 1.0 | |
| | | cosine_accuracy@10 | 1.0 | |
| | | cosine_precision@1 | 0.8696 | |
| | | cosine_precision@3 | 0.3333 | |
| | | cosine_precision@5 | 0.2 | |
| | | cosine_precision@10 | 0.1 | |
| | | cosine_recall@1 | 0.8696 | |
| | | cosine_recall@3 | 1.0 | |
| | | cosine_recall@5 | 1.0 | |
| | | cosine_recall@10 | 1.0 | |
| | | cosine_ndcg@10 | 0.9462 | |
| | | cosine_mrr@10 | 0.9275 | |
| | | **cosine_map@100** | **0.9275** | |
| | |
| | #### Information Retrieval |
| | * Dataset: `dim_256` |
| | * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
| |
|
| | | Metric | Value | |
| | |:--------------------|:-----------| |
| | | cosine_accuracy@1 | 0.8261 | |
| | | cosine_accuracy@3 | 1.0 | |
| | | cosine_accuracy@5 | 1.0 | |
| | | cosine_accuracy@10 | 1.0 | |
| | | cosine_precision@1 | 0.8261 | |
| | | cosine_precision@3 | 0.3333 | |
| | | cosine_precision@5 | 0.2 | |
| | | cosine_precision@10 | 0.1 | |
| | | cosine_recall@1 | 0.8261 | |
| | | cosine_recall@3 | 1.0 | |
| | | cosine_recall@5 | 1.0 | |
| | | cosine_recall@10 | 1.0 | |
| | | cosine_ndcg@10 | 0.9301 | |
| | | cosine_mrr@10 | 0.9058 | |
| | | **cosine_map@100** | **0.9058** | |
| | |
| | #### Information Retrieval |
| | * Dataset: `dim_128` |
| | * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
| | |
| | | Metric | Value | |
| | |:--------------------|:-----------| |
| | | cosine_accuracy@1 | 0.7826 | |
| | | cosine_accuracy@3 | 0.9565 | |
| | | cosine_accuracy@5 | 1.0 | |
| | | cosine_accuracy@10 | 1.0 | |
| | | cosine_precision@1 | 0.7826 | |
| | | cosine_precision@3 | 0.3188 | |
| | | cosine_precision@5 | 0.2 | |
| | | cosine_precision@10 | 0.1 | |
| | | cosine_recall@1 | 0.7826 | |
| | | cosine_recall@3 | 0.9565 | |
| | | cosine_recall@5 | 1.0 | |
| | | cosine_recall@10 | 1.0 | |
| | | cosine_ndcg@10 | 0.9092 | |
| | | cosine_mrr@10 | 0.8783 | |
| | | **cosine_map@100** | **0.8783** | |
| | |
| | #### Information Retrieval |
| | * Dataset: `dim_64` |
| | * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
| |
|
| | | Metric | Value | |
| | |:--------------------|:-----------| |
| | | cosine_accuracy@1 | 0.8261 | |
| | | cosine_accuracy@3 | 0.9565 | |
| | | cosine_accuracy@5 | 0.9565 | |
| | | cosine_accuracy@10 | 1.0 | |
| | | cosine_precision@1 | 0.8261 | |
| | | cosine_precision@3 | 0.3188 | |
| | | cosine_precision@5 | 0.1913 | |
| | | cosine_precision@10 | 0.1 | |
| | | cosine_recall@1 | 0.8261 | |
| | | cosine_recall@3 | 0.9565 | |
| | | cosine_recall@5 | 0.9565 | |
| | | cosine_recall@10 | 1.0 | |
| | | cosine_ndcg@10 | 0.9164 | |
| | | cosine_mrr@10 | 0.8895 | |
| | | **cosine_map@100** | **0.8895** | |
| | |
| | <!-- |
| | ## Bias, Risks and Limitations |
| | |
| | *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| | --> |
| | |
| | <!-- |
| | ### Recommendations |
| | |
| | *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| | --> |
| | |
| | ## Training Details |
| | |
| | ### Training Dataset |
| | |
| | #### json |
| | |
| | * Dataset: json |
| | * Size: 198 training samples |
| | * Columns: <code>positive</code> and <code>anchor</code> |
| | * Approximate statistics based on the first 198 samples: |
| | | | positive | anchor | |
| | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | | type | string | string | |
| | | details | <ul><li>min: 19 tokens</li><li>mean: 33.76 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.87 tokens</li><li>max: 21 tokens</li></ul> | |
| | * Samples: |
| | | positive | anchor | |
| | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------| |
| | | <code>Klađenje na ukupan broj poena timova podrazumeva predviđanje da li će jedan tim postići više ili manje poena od postavljene granice, nezavisno od konačnog ishoda.</code> | <code>Kako funkcioniše klađenje na ukupan broj poena timova?</code> | |
| | | <code>Konačan ishod podrazumeva klađenje na to ko će pobediti u utakmici, pri čemu postoje tri mogućnosti: pobeda domaćina, pobeda gosta ili nerešeno.</code> | <code>Šta znači klađenje na konačan ishod?</code> | |
| | | <code>Patent opklada uključuje tri događaja sa ukupno sedam pojedinačnih opklada: tri singl, tri dubl i jedna trostruka opklada.</code> | <code>Šta je patent opklada?</code> | |
| | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
| | ```json |
| | { |
| | "loss": "MultipleNegativesRankingLoss", |
| | "matryoshka_dims": [ |
| | 768, |
| | 512, |
| | 256, |
| | 128, |
| | 64 |
| | ], |
| | "matryoshka_weights": [ |
| | 1, |
| | 1, |
| | 1, |
| | 1, |
| | 1 |
| | ], |
| | "n_dims_per_step": -1 |
| | } |
| | ``` |
| | |
| | ### Training Hyperparameters |
| | #### Non-Default Hyperparameters |
| | |
| | - `eval_strategy`: epoch |
| | - `per_device_train_batch_size`: 32 |
| | - `per_device_eval_batch_size`: 16 |
| | - `gradient_accumulation_steps`: 16 |
| | - `learning_rate`: 2e-05 |
| | - `num_train_epochs`: 4 |
| | - `lr_scheduler_type`: cosine |
| | - `warmup_ratio`: 0.1 |
| | - `bf16`: True |
| | - `tf32`: False |
| | - `load_best_model_at_end`: True |
| | - `optim`: adamw_torch_fused |
| | - `batch_sampler`: no_duplicates |
| | |
| | #### All Hyperparameters |
| | <details><summary>Click to expand</summary> |
| | |
| | - `overwrite_output_dir`: False |
| | - `do_predict`: False |
| | - `eval_strategy`: epoch |
| | - `prediction_loss_only`: True |
| | - `per_device_train_batch_size`: 32 |
| | - `per_device_eval_batch_size`: 16 |
| | - `per_gpu_train_batch_size`: None |
| | - `per_gpu_eval_batch_size`: None |
| | - `gradient_accumulation_steps`: 16 |
| | - `eval_accumulation_steps`: None |
| | - `torch_empty_cache_steps`: None |
| | - `learning_rate`: 2e-05 |
| | - `weight_decay`: 0.0 |
| | - `adam_beta1`: 0.9 |
| | - `adam_beta2`: 0.999 |
| | - `adam_epsilon`: 1e-08 |
| | - `max_grad_norm`: 1.0 |
| | - `num_train_epochs`: 4 |
| | - `max_steps`: -1 |
| | - `lr_scheduler_type`: cosine |
| | - `lr_scheduler_kwargs`: {} |
| | - `warmup_ratio`: 0.1 |
| | - `warmup_steps`: 0 |
| | - `log_level`: passive |
| | - `log_level_replica`: warning |
| | - `log_on_each_node`: True |
| | - `logging_nan_inf_filter`: True |
| | - `save_safetensors`: True |
| | - `save_on_each_node`: False |
| | - `save_only_model`: False |
| | - `restore_callback_states_from_checkpoint`: False |
| | - `no_cuda`: False |
| | - `use_cpu`: False |
| | - `use_mps_device`: False |
| | - `seed`: 42 |
| | - `data_seed`: None |
| | - `jit_mode_eval`: False |
| | - `use_ipex`: False |
| | - `bf16`: True |
| | - `fp16`: False |
| | - `fp16_opt_level`: O1 |
| | - `half_precision_backend`: auto |
| | - `bf16_full_eval`: False |
| | - `fp16_full_eval`: False |
| | - `tf32`: False |
| | - `local_rank`: 0 |
| | - `ddp_backend`: None |
| | - `tpu_num_cores`: None |
| | - `tpu_metrics_debug`: False |
| | - `debug`: [] |
| | - `dataloader_drop_last`: False |
| | - `dataloader_num_workers`: 0 |
| | - `dataloader_prefetch_factor`: None |
| | - `past_index`: -1 |
| | - `disable_tqdm`: False |
| | - `remove_unused_columns`: True |
| | - `label_names`: None |
| | - `load_best_model_at_end`: True |
| | - `ignore_data_skip`: False |
| | - `fsdp`: [] |
| | - `fsdp_min_num_params`: 0 |
| | - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| | - `fsdp_transformer_layer_cls_to_wrap`: None |
| | - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| | - `deepspeed`: None |
| | - `label_smoothing_factor`: 0.0 |
| | - `optim`: adamw_torch_fused |
| | - `optim_args`: None |
| | - `adafactor`: False |
| | - `group_by_length`: False |
| | - `length_column_name`: length |
| | - `ddp_find_unused_parameters`: None |
| | - `ddp_bucket_cap_mb`: None |
| | - `ddp_broadcast_buffers`: False |
| | - `dataloader_pin_memory`: True |
| | - `dataloader_persistent_workers`: False |
| | - `skip_memory_metrics`: True |
| | - `use_legacy_prediction_loop`: False |
| | - `push_to_hub`: False |
| | - `resume_from_checkpoint`: None |
| | - `hub_model_id`: None |
| | - `hub_strategy`: every_save |
| | - `hub_private_repo`: False |
| | - `hub_always_push`: False |
| | - `gradient_checkpointing`: False |
| | - `gradient_checkpointing_kwargs`: None |
| | - `include_inputs_for_metrics`: False |
| | - `eval_do_concat_batches`: True |
| | - `fp16_backend`: auto |
| | - `push_to_hub_model_id`: None |
| | - `push_to_hub_organization`: None |
| | - `mp_parameters`: |
| | - `auto_find_batch_size`: False |
| | - `full_determinism`: False |
| | - `torchdynamo`: None |
| | - `ray_scope`: last |
| | - `ddp_timeout`: 1800 |
| | - `torch_compile`: False |
| | - `torch_compile_backend`: None |
| | - `torch_compile_mode`: None |
| | - `dispatch_batches`: None |
| | - `split_batches`: None |
| | - `include_tokens_per_second`: False |
| | - `include_num_input_tokens_seen`: False |
| | - `neftune_noise_alpha`: None |
| | - `optim_target_modules`: None |
| | - `batch_eval_metrics`: False |
| | - `eval_on_start`: False |
| | - `eval_use_gather_object`: False |
| | - `batch_sampler`: no_duplicates |
| | - `multi_dataset_batch_sampler`: proportional |
| | |
| | </details> |
| | |
| | ### Training Logs |
| | | Epoch | Step | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
| | |:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
| | | 1.0 | 1 | 0.6717 | 0.7663 | 0.8229 | 0.5755 | 0.8242 | |
| | | **2.0** | **2** | **0.7779** | **0.8457** | **0.8638** | **0.7833** | **0.8635** | |
| | | 3.0 | 4 | 0.8410 | 0.8732 | 0.8674 | 0.8167 | 0.8659 | |
| | | 1.0 | 1 | 0.8410 | 0.8732 | 0.8674 | 0.8167 | 0.8659 | |
| | | **2.0** | **2** | **0.8845** | **0.8732** | **0.9022** | **0.858** | **0.9022** | |
| | | 3.0 | 4 | 0.8783 | 0.9058 | 0.9275 | 0.8895 | 0.9022 | |
| |
|
| | * The bold row denotes the saved checkpoint. |
| |
|
| | ### Framework Versions |
| | - Python: 3.10.12 |
| | - Sentence Transformers: 3.1.0 |
| | - Transformers: 4.44.2 |
| | - PyTorch: 2.4.0+cu121 |
| | - Accelerate: 0.33.0 |
| | - Datasets: 3.0.0 |
| | - Tokenizers: 0.19.1 |
| |
|
| | ## 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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