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--- |
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base_model: BAAI/bge-m3 |
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language: |
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- hu |
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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 |
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- dot_accuracy |
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- manhattan_accuracy |
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- euclidean_accuracy |
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- max_accuracy |
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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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- generated_from_trainer |
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- dataset_size:200000 |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: Emberek várnak a lámpánál kerékpárral. |
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sentences: |
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- Az emberek piros lámpánál haladnak. |
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- Az emberek a kerékpárjukon vannak. |
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- Egy fekete kutya úszik a vízben egy teniszlabdával a szájában |
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- source_sentence: A kutya a vízben van. |
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sentences: |
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- Két férfi takarítja a havat a tetőről, az egyik egy emelőben ül, a másik pedig |
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a tetőn. |
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- A macska a vízben van, és dühös. |
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- Egy kutya van a vízben, a szájában egy faág. |
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- source_sentence: A nő feketét visel. |
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sentences: |
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- Egy barna kutya fröcsköl, ahogy úszik a vízben. |
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- Egy tetoválással rendelkező nő, aki fekete tank tetején néz a földre. |
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- 'Egy kékbe öltözött nő intenzív arckifejezéssel üti a teniszlabdát. A képen:' |
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- source_sentence: Az emberek alszanak. |
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sentences: |
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- Három ember beszélget egy városi utcán. |
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- A nő fehéret visel. |
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- Egy apa és a fia ölelgeti alvás közben. |
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- source_sentence: Az emberek alszanak. |
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sentences: |
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- Egy feketébe öltözött nő cigarettát és bevásárlótáskát tart a kezében, miközben |
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egy idősebb nő átmegy az utcán. |
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- Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy bokrok és egy |
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sor viktoriánus stílusú épület, melyek közül sokat a kép jobb oldalán lévő erős |
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elmosódás tesz kivehetetlenné. |
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- Egy apa és a fia ölelgeti alvás közben. |
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model-index: |
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- name: gte_hun |
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results: |
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- task: |
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type: triplet |
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name: Triplet |
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dataset: |
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name: all nli dev |
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type: all-nli-dev |
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metrics: |
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- type: cosine_accuracy |
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value: 0.979 |
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name: Cosine Accuracy |
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- type: dot_accuracy |
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value: 0.021 |
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name: Dot Accuracy |
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- type: manhattan_accuracy |
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value: 0.9804 |
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name: Manhattan Accuracy |
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- type: euclidean_accuracy |
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value: 0.979 |
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name: Euclidean Accuracy |
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- type: max_accuracy |
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value: 0.9804 |
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name: Max Accuracy |
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- task: |
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type: triplet |
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name: Triplet |
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dataset: |
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name: all nli test |
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type: all-nli-test |
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metrics: |
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- type: cosine_accuracy |
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value: 0.979 |
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name: Cosine Accuracy |
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- type: dot_accuracy |
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value: 0.021 |
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name: Dot Accuracy |
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- type: manhattan_accuracy |
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value: 0.9804 |
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name: Manhattan Accuracy |
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- type: euclidean_accuracy |
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value: 0.979 |
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name: Euclidean Accuracy |
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- type: max_accuracy |
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value: 0.9804 |
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name: Max Accuracy |
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--- |
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# gte_hun |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the train 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. |
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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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 --> |
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- **Maximum Sequence Length:** 8192 tokens |
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- **Output Dimensionality:** 1024 tokens |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- train |
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- **Language:** hu |
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- **License:** apache-2.0 |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
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(1): Pooling({'word_embedding_dimension': 1024, '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}) |
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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("karsar/bge-m3-hu") |
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# Run inference |
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sentences = [ |
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'Az emberek alszanak.', |
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'Egy apa és a fia ölelgeti alvás közben.', |
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'Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy bokrok és egy sor viktoriánus stílusú épület, melyek közül sokat a kép jobb oldalán lévő erős elmosódás tesz kivehetetlenné.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### 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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#### Triplet |
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* Dataset: `all-nli-dev` |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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| Metric | Value | |
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|:-------------------|:-----------| |
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| cosine_accuracy | 0.979 | |
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| dot_accuracy | 0.021 | |
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| manhattan_accuracy | 0.9804 | |
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| euclidean_accuracy | 0.979 | |
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| **max_accuracy** | **0.9804** | |
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#### Triplet |
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* Dataset: `all-nli-test` |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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| Metric | Value | |
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|:-------------------|:-----------| |
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| cosine_accuracy | 0.979 | |
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| dot_accuracy | 0.021 | |
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| manhattan_accuracy | 0.9804 | |
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| euclidean_accuracy | 0.979 | |
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| **max_accuracy** | **0.9804** | |
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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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#### train |
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* Dataset: train |
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* Size: 200,000 training samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 11.73 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.24 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.07 tokens</li><li>max: 53 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:---------------------------------------------------------------------------|:----------------------------------------------|:---------------------------------------------------------------| |
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| <code>Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett.</code> | <code>Egy ember a szabadban, lóháton.</code> | <code>Egy ember egy étteremben van, és omlettet rendel.</code> | |
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| <code>Gyerekek mosolyogva és integetett a kamera</code> | <code>Gyermekek vannak jelen</code> | <code>A gyerekek homlokot rántanak</code> | |
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| <code>Egy fiú ugrál a gördeszkát a közepén egy piros híd.</code> | <code>A fiú gördeszkás trükköt csinál.</code> | <code>A fiú korcsolyázik a járdán.</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Evaluation Dataset |
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#### train |
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* Dataset: train |
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* Size: 5,000 evaluation samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 11.73 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.24 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.07 tokens</li><li>max: 53 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:---------------------------------------------------------------------------|:----------------------------------------------|:---------------------------------------------------------------| |
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| <code>Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett.</code> | <code>Egy ember a szabadban, lóháton.</code> | <code>Egy ember egy étteremben van, és omlettet rendel.</code> | |
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| <code>Gyerekek mosolyogva és integetett a kamera</code> | <code>Gyermekek vannak jelen</code> | <code>A gyerekek homlokot rántanak</code> | |
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| <code>Egy fiú ugrál a gördeszkát a közepén egy piros híd.</code> | <code>A fiú gördeszkás trükköt csinál.</code> | <code>A fiú korcsolyázik a járdán.</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `num_train_epochs`: 1 |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `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`: 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`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-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`: 0.1 |
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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`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `eval_use_gather_object`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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|
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</details> |
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|
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### Training Logs |
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<details><summary>Click to expand</summary> |
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|
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| Epoch | Step | Training Loss | train loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy | |
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|:-----:|:-----:|:-------------:|:----------:|:------------------------:|:-------------------------:| |
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| 0 | 0 | - | - | 0.7176 | - | |
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| 0.008 | 100 | 1.0753 | - | - | - | |
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| 0.016 | 200 | 0.7611 | - | - | - | |
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| 0.024 | 300 | 1.0113 | - | - | - | |
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| 0.032 | 400 | 0.6224 | - | - | - | |
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| 0.04 | 500 | 0.8465 | 0.6159 | 0.8938 | - | |
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| 0.048 | 600 | 0.7761 | - | - | - | |
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| 0.056 | 700 | 0.8738 | - | - | - | |
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| 0.064 | 800 | 0.9393 | - | - | - | |
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| 0.072 | 900 | 0.9743 | - | - | - | |
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| 0.08 | 1000 | 0.8445 | 0.4556 | 0.8916 | - | |
|
| 0.088 | 1100 | 0.7237 | - | - | - | |
|
| 0.096 | 1200 | 0.8064 | - | - | - | |
|
| 0.104 | 1300 | 0.607 | - | - | - | |
|
| 0.112 | 1400 | 0.7632 | - | - | - | |
|
| 0.12 | 1500 | 0.7477 | 1.6880 | 0.6748 | - | |
|
| 0.128 | 1600 | 1.018 | - | - | - | |
|
| 0.136 | 1700 | 0.9046 | - | - | - | |
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| 0.144 | 1800 | 0.728 | - | - | - | |
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| 0.152 | 1900 | 0.7219 | - | - | - | |
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| 0.16 | 2000 | 0.632 | 0.6459 | 0.8622 | - | |
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| 0.168 | 2100 | 0.6067 | - | - | - | |
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| 0.176 | 2200 | 0.7267 | - | - | - | |
|
| 0.184 | 2300 | 0.781 | - | - | - | |
|
| 0.192 | 2400 | 0.662 | - | - | - | |
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| 0.2 | 2500 | 0.6192 | 1.0124 | 0.8328 | - | |
|
| 0.208 | 2600 | 0.7943 | - | - | - | |
|
| 0.216 | 2700 | 0.8762 | - | - | - | |
|
| 0.224 | 2800 | 0.7913 | - | - | - | |
|
| 0.232 | 2900 | 0.8049 | - | - | - | |
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| 0.24 | 3000 | 0.858 | 0.6378 | 0.8046 | - | |
|
| 0.248 | 3100 | 0.679 | - | - | - | |
|
| 0.256 | 3200 | 0.7213 | - | - | - | |
|
| 0.264 | 3300 | 0.6028 | - | - | - | |
|
| 0.272 | 3400 | 0.5778 | - | - | - | |
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| 0.28 | 3500 | 0.5434 | 0.6784 | 0.8496 | - | |
|
| 0.288 | 3600 | 0.6726 | - | - | - | |
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| 0.296 | 3700 | 0.7347 | - | - | - | |
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| 0.304 | 3800 | 0.8413 | - | - | - | |
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| 0.312 | 3900 | 0.7993 | - | - | - | |
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| 0.32 | 4000 | 0.8899 | 0.7732 | 0.8092 | - | |
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| 0.328 | 4100 | 1.1505 | - | - | - | |
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| 0.336 | 4200 | 0.8871 | - | - | - | |
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| 0.344 | 4300 | 0.8423 | - | - | - | |
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| 0.352 | 4400 | 0.8288 | - | - | - | |
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| 0.36 | 4500 | 0.6728 | 0.6341 | 0.8436 | - | |
|
| 0.368 | 4600 | 0.7534 | - | - | - | |
|
| 0.376 | 4700 | 0.8276 | - | - | - | |
|
| 0.384 | 4800 | 0.7677 | - | - | - | |
|
| 0.392 | 4900 | 0.588 | - | - | - | |
|
| 0.4 | 5000 | 0.7742 | 0.4389 | 0.8808 | - | |
|
| 0.408 | 5100 | 0.6782 | - | - | - | |
|
| 0.416 | 5200 | 0.6688 | - | - | - | |
|
| 0.424 | 5300 | 0.5579 | - | - | - | |
|
| 0.432 | 5400 | 0.6891 | - | - | - | |
|
| 0.44 | 5500 | 0.5764 | 0.4192 | 0.902 | - | |
|
| 0.448 | 5600 | 0.6152 | - | - | - | |
|
| 0.456 | 5700 | 0.6864 | - | - | - | |
|
| 0.464 | 5800 | 0.6429 | - | - | - | |
|
| 0.472 | 5900 | 0.9379 | - | - | - | |
|
| 0.48 | 6000 | 0.7607 | 0.4744 | 0.8736 | - | |
|
| 0.488 | 6100 | 0.819 | - | - | - | |
|
| 0.496 | 6200 | 0.6316 | - | - | - | |
|
| 0.504 | 6300 | 0.8175 | - | - | - | |
|
| 0.512 | 6400 | 0.8485 | - | - | - | |
|
| 0.52 | 6500 | 0.5374 | 0.4860 | 0.916 | - | |
|
| 0.528 | 6600 | 0.781 | - | - | - | |
|
| 0.536 | 6700 | 0.7722 | - | - | - | |
|
| 0.544 | 6800 | 0.7281 | - | - | - | |
|
| 0.552 | 6900 | 0.8453 | - | - | - | |
|
| 0.56 | 7000 | 0.8541 | 0.2612 | 0.9322 | - | |
|
| 0.568 | 7100 | 0.9698 | - | - | - | |
|
| 0.576 | 7200 | 0.7184 | - | - | - | |
|
| 0.584 | 7300 | 0.699 | - | - | - | |
|
| 0.592 | 7400 | 0.5574 | - | - | - | |
|
| 0.6 | 7500 | 0.5374 | 0.1939 | 0.9472 | - | |
|
| 0.608 | 7600 | 0.6485 | - | - | - | |
|
| 0.616 | 7700 | 0.5177 | - | - | - | |
|
| 0.624 | 7800 | 0.814 | - | - | - | |
|
| 0.632 | 7900 | 0.6442 | - | - | - | |
|
| 0.64 | 8000 | 0.5301 | 0.1192 | 0.9616 | - | |
|
| 0.648 | 8100 | 0.4948 | - | - | - | |
|
| 0.656 | 8200 | 0.426 | - | - | - | |
|
| 0.664 | 8300 | 0.4781 | - | - | - | |
|
| 0.672 | 8400 | 0.4188 | - | - | - | |
|
| 0.68 | 8500 | 0.5695 | 0.1523 | 0.9492 | - | |
|
| 0.688 | 8600 | 0.3895 | - | - | - | |
|
| 0.696 | 8700 | 0.5041 | - | - | - | |
|
| 0.704 | 8800 | 0.7599 | - | - | - | |
|
| 0.712 | 8900 | 0.5893 | - | - | - | |
|
| 0.72 | 9000 | 0.6678 | 0.1363 | 0.9588 | - | |
|
| 0.728 | 9100 | 0.5917 | - | - | - | |
|
| 0.736 | 9200 | 0.6201 | - | - | - | |
|
| 0.744 | 9300 | 0.5072 | - | - | - | |
|
| 0.752 | 9400 | 0.4233 | - | - | - | |
|
| 0.76 | 9500 | 0.396 | 0.2490 | 0.937 | - | |
|
| 0.768 | 9600 | 0.3699 | - | - | - | |
|
| 0.776 | 9700 | 0.3734 | - | - | - | |
|
| 0.784 | 9800 | 0.4145 | - | - | - | |
|
| 0.792 | 9900 | 0.4422 | - | - | - | |
|
| 0.8 | 10000 | 0.4427 | 0.1394 | 0.9634 | - | |
|
| 0.808 | 10100 | 0.678 | - | - | - | |
|
| 0.816 | 10200 | 0.6771 | - | - | - | |
|
| 0.824 | 10300 | 0.8249 | - | - | - | |
|
| 0.832 | 10400 | 0.5003 | - | - | - | |
|
| 0.84 | 10500 | 0.5586 | 0.1006 | 0.9726 | - | |
|
| 0.848 | 10600 | 0.4649 | - | - | - | |
|
| 0.856 | 10700 | 0.5322 | - | - | - | |
|
| 0.864 | 10800 | 0.4837 | - | - | - | |
|
| 0.872 | 10900 | 0.5717 | - | - | - | |
|
| 0.88 | 11000 | 0.4403 | 0.1009 | 0.9688 | - | |
|
| 0.888 | 11100 | 0.5044 | - | - | - | |
|
| 0.896 | 11200 | 0.4771 | - | - | - | |
|
| 0.904 | 11300 | 0.4426 | - | - | - | |
|
| 0.912 | 11400 | 0.3705 | - | - | - | |
|
| 0.92 | 11500 | 0.4445 | 0.0992 | 0.978 | - | |
|
| 0.928 | 11600 | 0.3707 | - | - | - | |
|
| 0.936 | 11700 | 0.4322 | - | - | - | |
|
| 0.944 | 11800 | 0.4619 | - | - | - | |
|
| 0.952 | 11900 | 0.4772 | - | - | - | |
|
| 0.96 | 12000 | 0.5756 | 0.0950 | 0.9804 | - | |
|
| 0.968 | 12100 | 0.5649 | - | - | - | |
|
| 0.976 | 12200 | 0.5037 | - | - | - | |
|
| 0.984 | 12300 | 0.0317 | - | - | - | |
|
| 0.992 | 12400 | 0.0001 | - | - | - | |
|
| 1.0 | 12500 | 0.0001 | 0.0948 | 0.9804 | 0.9804 | |
|
|
|
</details> |
|
|
|
### Framework Versions |
|
- Python: 3.11.8 |
|
- Sentence Transformers: 3.1.1 |
|
- Transformers: 4.44.0 |
|
- PyTorch: 2.3.0.post101 |
|
- Accelerate: 0.33.0 |
|
- Datasets: 2.18.0 |
|
- Tokenizers: 0.19.0 |
|
|
|
## 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", |
|
} |
|
``` |
|
|
|
#### 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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