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--- |
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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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:857856 |
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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: paraphrase-multilingual-MiniLM-L12-v2 |
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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: 0.992 |
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name: Cosine Accuracy |
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- type: dot_accuracy |
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value: 0.0108 |
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name: Dot Accuracy |
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- type: manhattan_accuracy |
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value: 0.9908 |
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name: Manhattan Accuracy |
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- type: euclidean_accuracy |
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value: 0.9908 |
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name: Euclidean Accuracy |
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- type: max_accuracy |
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value: 0.992 |
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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: 0.9913636363636363 |
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name: Cosine Accuracy |
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- type: dot_accuracy |
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value: 0.013939393939393939 |
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name: Dot Accuracy |
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- type: manhattan_accuracy |
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value: 0.990909090909091 |
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name: Manhattan Accuracy |
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- type: euclidean_accuracy |
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value: 0.9910606060606061 |
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name: Euclidean Accuracy |
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- type: max_accuracy |
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value: 0.9913636363636363 |
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name: Max Accuracy |
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--- |
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# paraphrase-multilingual-MiniLM-L12-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the train dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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|
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision ae06c001a2546bef168b9bf8f570ccb1a16aaa27 --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 384 tokens |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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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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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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) |
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``` |
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## 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/paraphrase-multilingual-MiniLM-L12-hu_v1") |
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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, 384] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.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 | 0.992 | |
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| dot_accuracy | 0.0108 | |
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| manhattan_accuracy | 0.9908 | |
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| euclidean_accuracy | 0.9908 | |
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| **max_accuracy** | **0.992** | |
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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 | 0.9914 | |
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| dot_accuracy | 0.0139 | |
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| manhattan_accuracy | 0.9909 | |
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| euclidean_accuracy | 0.9911 | |
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| **max_accuracy** | **0.9914** | |
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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: 857,856 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`: 128 |
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- `per_device_eval_batch_size`: 128 |
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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`: 128 |
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- `per_device_eval_batch_size`: 128 |
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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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### Training Logs |
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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 | - | - | 0.7574 | - | |
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| 0.0149 | 100 | 2.5002 | - | - | - | |
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| 0.0298 | 200 | 1.9984 | - | - | - | |
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| 0.0448 | 300 | 1.8094 | - | - | - | |
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| 0.0597 | 400 | 1.6704 | - | - | - | |
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| 0.0746 | 500 | 1.5518 | - | - | - | |
|
| 0.0895 | 600 | 1.449 | - | - | - | |
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| 0.1044 | 700 | 1.5998 | - | - | - | |
|
| 0.1194 | 800 | 1.5725 | - | - | - | |
|
| 0.1343 | 900 | 1.5341 | - | - | - | |
|
| 0.1492 | 1000 | 1.3423 | - | - | - | |
|
| 0.1641 | 1100 | 1.2485 | - | - | - | |
|
| 0.1791 | 1200 | 1.1527 | - | - | - | |
|
| 0.1940 | 1300 | 1.1672 | - | - | - | |
|
| 0.2089 | 1400 | 1.2426 | - | - | - | |
|
| 0.2238 | 1500 | 1.0948 | - | - | - | |
|
| 0.2387 | 1600 | 1.0069 | - | - | - | |
|
| 0.2537 | 1700 | 0.976 | - | - | - | |
|
| 0.2686 | 1800 | 0.897 | - | - | - | |
|
| 0.2835 | 1900 | 0.7825 | - | - | - | |
|
| 0.2984 | 2000 | 0.9421 | 0.1899 | 0.9568 | - | |
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| 0.3133 | 2100 | 0.8651 | - | - | - | |
|
| 0.3283 | 2200 | 0.8184 | - | - | - | |
|
| 0.3432 | 2300 | 0.699 | - | - | - | |
|
| 0.3581 | 2400 | 0.6704 | - | - | - | |
|
| 0.3730 | 2500 | 0.6477 | - | - | - | |
|
| 0.3879 | 2600 | 0.7077 | - | - | - | |
|
| 0.4029 | 2700 | 0.7364 | - | - | - | |
|
| 0.4178 | 2800 | 0.665 | - | - | - | |
|
| 0.4327 | 2900 | 1.2512 | - | - | - | |
|
| 0.4476 | 3000 | 1.3693 | - | - | - | |
|
| 0.4625 | 3100 | 1.3959 | - | - | - | |
|
| 0.4775 | 3200 | 1.4175 | - | - | - | |
|
| 0.4924 | 3300 | 1.402 | - | - | - | |
|
| 0.5073 | 3400 | 1.3832 | - | - | - | |
|
| 0.5222 | 3500 | 1.3671 | - | - | - | |
|
| 0.5372 | 3600 | 1.3666 | - | - | - | |
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| 0.5521 | 3700 | 1.3479 | - | - | - | |
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| 0.5670 | 3800 | 1.3272 | - | - | - | |
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| 0.5819 | 3900 | 1.3353 | - | - | - | |
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| 0.5968 | 4000 | 1.3177 | 0.0639 | 0.9902 | - | |
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| 0.6118 | 4100 | 1.3068 | - | - | - | |
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| 0.6267 | 4200 | 1.3054 | - | - | - | |
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| 0.6416 | 4300 | 1.3098 | - | - | - | |
|
| 0.6565 | 4400 | 1.2839 | - | - | - | |
|
| 0.6714 | 4500 | 1.2976 | - | - | - | |
|
| 0.6864 | 4600 | 1.2669 | - | - | - | |
|
| 0.7013 | 4700 | 1.208 | - | - | - | |
|
| 0.7162 | 4800 | 1.194 | - | - | - | |
|
| 0.7311 | 4900 | 1.1974 | - | - | - | |
|
| 0.7460 | 5000 | 1.1834 | - | - | - | |
|
| 0.7610 | 5100 | 1.1876 | - | - | - | |
|
| 0.7759 | 5200 | 1.1743 | - | - | - | |
|
| 0.7908 | 5300 | 1.1839 | - | - | - | |
|
| 0.8057 | 5400 | 1.1778 | - | - | - | |
|
| 0.8207 | 5500 | 1.1711 | - | - | - | |
|
| 0.8356 | 5600 | 1.1809 | - | - | - | |
|
| 0.8505 | 5700 | 1.1825 | - | - | - | |
|
| 0.8654 | 5800 | 1.1795 | - | - | - | |
|
| 0.8803 | 5900 | 1.1788 | - | - | - | |
|
| 0.8953 | 6000 | 1.1819 | 0.0371 | 0.992 | - | |
|
| 0.9102 | 6100 | 1.1741 | - | - | - | |
|
| 0.9251 | 6200 | 1.1871 | - | - | - | |
|
| 0.9400 | 6300 | 0.498 | - | - | - | |
|
| 0.9549 | 6400 | 0.093 | - | - | - | |
|
| 0.9699 | 6500 | 0.1597 | - | - | - | |
|
| 0.9848 | 6600 | 0.2033 | - | - | - | |
|
| 0.9997 | 6700 | 0.16 | - | - | - | |
|
| 1.0 | 6702 | - | - | - | 0.9914 | |
|
|
|
|
|
### 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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} |
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``` |
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