|
--- |
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base_model: BAAI/bge-base-en-v1.5 |
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language: |
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- en |
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library_name: sentence-transformers |
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license: apache-2.0 |
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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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pipeline_tag: sentence-similarity |
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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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- dataset_size:1K<n<10K |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: Herkules na rozstajach |
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sentences: |
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- jak zinterpretować wymowę obrazu Herkules na rozstajach? |
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- w jakim celu nowożeńcom w Korei wręcza się injeolmi? |
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- z jakiego powodu zwołano synod w Whitby? |
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- source_sentence: gdzie rośnie bokkonia? |
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sentences: |
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- gdzie występuje rogownica szerokolistna? |
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- Dłutowanie metodą Maaga Struganie metodą Sunderlanda |
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- kim byli beatyfikowani przez papieża Jana Pawła II męczennicy z Almerii? |
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- source_sentence: kto walczył o Brisbane? |
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sentences: |
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- Szczurza gorączka TAM Gorączka od ugryzienia szczura |
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- Szczurza gorączka TAM Gorączka od ugryzienia szczura |
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- który nadworny fotograf sprzedał swój patent firmie Eastman Kodak? |
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- source_sentence: Morskie Oko (kabaret) |
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sentences: |
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- jak skończył się spór o Morskie Oko? |
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- ile razy Srebrna Biblia była przywożona do Szwecji? |
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- W latach 1955–1956 część więźniów przebywających w Spassku zwolniono. |
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- source_sentence: ile katod ma duodioda? |
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sentences: |
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- kto nosi mantyle? |
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- w jakim celu nowożeńcom w Korei wręcza się injeolmi? |
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- W latach 1955–1956 część więźniów przebywających w Spassku zwolniono. |
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model-index: |
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- name: bge-base-en-v1.5-klej-dyk |
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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: dim 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.20432692307692307 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.5024038461538461 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.6802884615384616 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.7548076923076923 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.20432692307692307 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.1674679487179487 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.1360576923076923 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.07548076923076923 |
|
name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.20432692307692307 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
|
value: 0.5024038461538461 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.6802884615384616 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7548076923076923 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.4741957684261531 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.3839495573870572 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.3909524912840153 |
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name: Cosine Map@100 |
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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: dim 512 |
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type: dim_512 |
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metrics: |
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- type: cosine_accuracy@1 |
|
value: 0.19471153846153846 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.49278846153846156 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6634615384615384 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7548076923076923 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.19471153846153846 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1642628205128205 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.13269230769230766 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07548076923076921 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.19471153846153846 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.49278846153846156 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6634615384615384 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7548076923076923 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.4648228460121699 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.37225847069597073 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.378344181427981 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
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metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.18990384615384615 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.4543269230769231 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6057692307692307 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7067307692307693 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.18990384615384615 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.15144230769230768 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.12115384615384615 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07067307692307692 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.18990384615384615 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.4543269230769231 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6057692307692307 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7067307692307693 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.437691661658994 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.3522741147741148 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.35902651881139014 |
|
name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
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name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
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type: dim_128 |
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metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.18509615384615385 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.4375 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.5480769230769231 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.6442307692307693 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.18509615384615385 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.14583333333333331 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1096153846153846 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.06442307692307692 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.18509615384615385 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.4375 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.5480769230769231 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.6442307692307693 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.4084493303372093 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.33323508089133086 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.3393128348021269 |
|
name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
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metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.17307692307692307 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.3389423076923077 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.4254807692307692 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5144230769230769 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.17307692307692307 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.11298076923076923 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.08509615384615386 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05144230769230769 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.17307692307692307 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3389423076923077 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.4254807692307692 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5144230769230769 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.333723313431585 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.2768763354700855 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2853193687152632 |
|
name: Cosine Map@100 |
|
--- |
|
|
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# bge-base-en-v1.5-klej-dyk |
|
|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
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- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
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- **Language:** en |
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- **License:** apache-2.0 |
|
|
|
### 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( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
(2): Normalize() |
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) |
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``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
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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("sentence_transformers_model_id") |
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# Run inference |
|
sentences = [ |
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'ile katod ma duodioda?', |
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'kto nosi mantyle?', |
|
'w jakim celu nowożeńcom w Korei wręcza się injeolmi?', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
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# [3, 768] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
|
|
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<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
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</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
|
|
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You can finetune this model on your own dataset. |
|
|
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<details><summary>Click to expand</summary> |
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|
|
</details> |
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--> |
|
|
|
<!-- |
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### Out-of-Scope Use |
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|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_768` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:----------| |
|
| cosine_accuracy@1 | 0.2043 | |
|
| cosine_accuracy@3 | 0.5024 | |
|
| cosine_accuracy@5 | 0.6803 | |
|
| cosine_accuracy@10 | 0.7548 | |
|
| cosine_precision@1 | 0.2043 | |
|
| cosine_precision@3 | 0.1675 | |
|
| cosine_precision@5 | 0.1361 | |
|
| cosine_precision@10 | 0.0755 | |
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| cosine_recall@1 | 0.2043 | |
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| cosine_recall@3 | 0.5024 | |
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| cosine_recall@5 | 0.6803 | |
|
| cosine_recall@10 | 0.7548 | |
|
| cosine_ndcg@10 | 0.4742 | |
|
| cosine_mrr@10 | 0.3839 | |
|
| **cosine_map@100** | **0.391** | |
|
|
|
#### 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 | 0.1947 | |
|
| cosine_accuracy@3 | 0.4928 | |
|
| cosine_accuracy@5 | 0.6635 | |
|
| cosine_accuracy@10 | 0.7548 | |
|
| cosine_precision@1 | 0.1947 | |
|
| cosine_precision@3 | 0.1643 | |
|
| cosine_precision@5 | 0.1327 | |
|
| cosine_precision@10 | 0.0755 | |
|
| cosine_recall@1 | 0.1947 | |
|
| cosine_recall@3 | 0.4928 | |
|
| cosine_recall@5 | 0.6635 | |
|
| cosine_recall@10 | 0.7548 | |
|
| cosine_ndcg@10 | 0.4648 | |
|
| cosine_mrr@10 | 0.3723 | |
|
| **cosine_map@100** | **0.3783** | |
|
|
|
#### 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 | 0.1899 | |
|
| cosine_accuracy@3 | 0.4543 | |
|
| cosine_accuracy@5 | 0.6058 | |
|
| cosine_accuracy@10 | 0.7067 | |
|
| cosine_precision@1 | 0.1899 | |
|
| cosine_precision@3 | 0.1514 | |
|
| cosine_precision@5 | 0.1212 | |
|
| cosine_precision@10 | 0.0707 | |
|
| cosine_recall@1 | 0.1899 | |
|
| cosine_recall@3 | 0.4543 | |
|
| cosine_recall@5 | 0.6058 | |
|
| cosine_recall@10 | 0.7067 | |
|
| cosine_ndcg@10 | 0.4377 | |
|
| cosine_mrr@10 | 0.3523 | |
|
| **cosine_map@100** | **0.359** | |
|
|
|
#### 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 | 0.1851 | |
|
| cosine_accuracy@3 | 0.4375 | |
|
| cosine_accuracy@5 | 0.5481 | |
|
| cosine_accuracy@10 | 0.6442 | |
|
| cosine_precision@1 | 0.1851 | |
|
| cosine_precision@3 | 0.1458 | |
|
| cosine_precision@5 | 0.1096 | |
|
| cosine_precision@10 | 0.0644 | |
|
| cosine_recall@1 | 0.1851 | |
|
| cosine_recall@3 | 0.4375 | |
|
| cosine_recall@5 | 0.5481 | |
|
| cosine_recall@10 | 0.6442 | |
|
| cosine_ndcg@10 | 0.4084 | |
|
| cosine_mrr@10 | 0.3332 | |
|
| **cosine_map@100** | **0.3393** | |
|
|
|
#### 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 | 0.1731 | |
|
| cosine_accuracy@3 | 0.3389 | |
|
| cosine_accuracy@5 | 0.4255 | |
|
| cosine_accuracy@10 | 0.5144 | |
|
| cosine_precision@1 | 0.1731 | |
|
| cosine_precision@3 | 0.113 | |
|
| cosine_precision@5 | 0.0851 | |
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| cosine_precision@10 | 0.0514 | |
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| cosine_recall@1 | 0.1731 | |
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| cosine_recall@3 | 0.3389 | |
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| cosine_recall@5 | 0.4255 | |
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| cosine_recall@10 | 0.5144 | |
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| cosine_ndcg@10 | 0.3337 | |
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| cosine_mrr@10 | 0.2769 | |
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| **cosine_map@100** | **0.2853** | |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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## Training Details |
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|
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 3,738 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 89.95 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 30.73 tokens</li><li>max: 76 tokens</li></ul> | |
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* Samples: |
|
| positive | anchor | |
|
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| <code>Rynek Kolumna Matki Boskiej, tzw. Kolumna Maryjna wykonana w latach 1725-1727 przez Johanna Melchiora Österreicha.</code> | <code>kto jest autorem kolumny maryjnej na raciborskim rynku?</code> | |
|
| <code>Chleb razowy jest ciemniejszy i zawiera większą ilość błonnika oraz składników mineralnych niż chleb biały (pytlowy, czyli wypiekany z mąki przesiewanej przez pytel), bowiem jest w nim większy udział drobin pochodzących z łupin ziarna, gdzie gromadzą się te składniki.</code> | <code>które składniki razowego chleba odpowiadają za jego walory zdrowotne?</code> | |
|
| <code>Najgłębsza znana studnia krasowa to jaskinia Vrtoglavica w Słowenii o głębokości ponad 600 metrów.</code> | <code>ile metrów głębokości mierzy studnia na podwórzu klasztoru w Czernej?</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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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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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> |
|
|
|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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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`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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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`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: True |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_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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- `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 |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | 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 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.6838 | 10 | 6.5594 | - | - | - | - | - | |
|
| 0.9573 | 14 | - | 0.3319 | 0.3751 | 0.3955 | 0.2618 | 0.4033 | |
|
| 1.3675 | 20 | 4.2206 | - | - | - | - | - | |
|
| 1.9829 | 29 | - | 0.3324 | 0.3591 | 0.3807 | 0.2833 | 0.3946 | |
|
| 2.0513 | 30 | 3.3414 | - | - | - | - | - | |
|
| 2.7350 | 40 | 2.9757 | - | - | - | - | - | |
|
| 2.9402 | 43 | - | 0.3375 | 0.3570 | 0.3805 | 0.2840 | 0.3905 | |
|
| 3.4188 | 50 | 2.8884 | - | - | - | - | - | |
|
| **3.8291** | **56** | **-** | **0.3393** | **0.359** | **0.3783** | **0.2853** | **0.391** | |
|
|
|
* The bold row denotes the saved checkpoint. |
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|
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### Framework Versions |
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- Python: 3.12.2 |
|
- Sentence Transformers: 3.0.0 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.3.1 |
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- Accelerate: 0.27.2 |
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- Datasets: 2.19.1 |
|
- Tokenizers: 0.19.1 |
|
|
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## Citation |
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### BibTeX |
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|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
|
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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} |
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``` |
|
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*Clearly define terms in order to be accessible across audiences.* |
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