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--- |
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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datasets: [] |
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language: [] |
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library_name: sentence-transformers |
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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:8408 |
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- loss:CosineSimilarityLoss |
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widget: |
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- source_sentence: president |
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sentences: |
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- assistante de banque priv e banco santander rio |
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- worldwide executive vice president corindus a siemens healthineers company |
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- soporte t cnico superior |
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- source_sentence: chief business strategy officer |
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sentences: |
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- sub jefe |
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- analista senior recursos humanos sales staff and logistics |
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- subgerente sostenibilidad y hseq |
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- source_sentence: gerente de planificaci贸n |
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sentences: |
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- analista de soporte web |
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- director |
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- gestion calidad |
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- source_sentence: global human resources leader |
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sentences: |
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- director manufacturing engineering |
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- quality specialist |
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- asesoramiento para comprar inmuebles en uruguay paraguay espa a y usa |
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- source_sentence: commercial manager |
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sentences: |
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- jefe de turno planta envasado de vinos |
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- gerente de operaciones |
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- vice president of finance americas |
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--- |
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# SentenceTransformer based on sentence-transformers/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). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb --> |
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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:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/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': 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 馃 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'commercial manager', |
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'gerente de operaciones', |
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'vice president of finance americas', |
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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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### Direct Usage (Transformers) |
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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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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 8,408 training samples |
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | label | |
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|:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 6.2 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.75 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.06</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | label | |
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|:----------------------------------------|:------------------------------------------------------------------------------|:-----------------| |
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| <code>strategic planning manager</code> | <code>senior brand manager uap southern cone & personal care cdm chile</code> | <code>0.0</code> | |
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| <code>director de planificacion</code> | <code>key account manager tiendas paris</code> | <code>0.0</code> | |
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| <code>gerente general</code> | <code>analista de cobranza</code> | <code>0.0</code> | |
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* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
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```json |
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{ |
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"loss_fct": "torch.nn.modules.loss.MSELoss" |
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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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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `num_train_epochs`: 50 |
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- `multi_dataset_batch_sampler`: round_robin |
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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`: no |
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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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- `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 |
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- `num_train_epochs`: 50 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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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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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | |
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|:-------:|:-----:|:-------------:| |
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| 0.9506 | 500 | 0.0434 | |
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| 1.9011 | 1000 | 0.0135 | |
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| 2.8517 | 1500 | 0.0072 | |
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| 3.8023 | 2000 | 0.0056 | |
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| 4.7529 | 2500 | 0.0044 | |
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| 5.7034 | 3000 | 0.0038 | |
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| 6.6540 | 3500 | 0.0034 | |
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| 7.6046 | 4000 | 0.0032 | |
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| 8.5551 | 4500 | 0.0029 | |
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| 9.5057 | 5000 | 0.0028 | |
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| 10.4563 | 5500 | 0.0026 | |
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| 11.4068 | 6000 | 0.0025 | |
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| 12.3574 | 6500 | 0.0026 | |
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| 13.3080 | 7000 | 0.0023 | |
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| 14.2586 | 7500 | 0.0023 | |
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| 15.2091 | 8000 | 0.0023 | |
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| 16.1597 | 8500 | 0.0022 | |
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| 17.1103 | 9000 | 0.0021 | |
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| 18.0608 | 9500 | 0.0019 | |
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| 19.0114 | 10000 | 0.0021 | |
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| 19.9620 | 10500 | 0.0019 | |
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| 20.9125 | 11000 | 0.0019 | |
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| 21.8631 | 11500 | 0.0016 | |
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| 22.8137 | 12000 | 0.0018 | |
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| 23.7643 | 12500 | 0.0018 | |
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| 24.7148 | 13000 | 0.0018 | |
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| 25.6654 | 13500 | 0.0016 | |
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| 26.6160 | 14000 | 0.0017 | |
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| 27.5665 | 14500 | 0.0016 | |
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| 28.5171 | 15000 | 0.0016 | |
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| 29.4677 | 15500 | 0.0016 | |
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| 30.4183 | 16000 | 0.0016 | |
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| 31.3688 | 16500 | 0.0019 | |
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| 32.3194 | 17000 | 0.0018 | |
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| 33.2700 | 17500 | 0.0017 | |
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| 34.2205 | 18000 | 0.0016 | |
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| 35.1711 | 18500 | 0.0016 | |
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| 36.1217 | 19000 | 0.0016 | |
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| 37.0722 | 19500 | 0.0015 | |
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| 38.0228 | 20000 | 0.0012 | |
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| 38.9734 | 20500 | 0.0015 | |
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| 39.9240 | 21000 | 0.0015 | |
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| 40.8745 | 21500 | 0.0013 | |
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| 41.8251 | 22000 | 0.0014 | |
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| 42.7757 | 22500 | 0.0014 | |
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| 43.7262 | 23000 | 0.0014 | |
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| 44.6768 | 23500 | 0.0013 | |
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| 45.6274 | 24000 | 0.0012 | |
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| 46.5779 | 24500 | 0.0014 | |
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| 47.5285 | 25000 | 0.0012 | |
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| 48.4791 | 25500 | 0.0013 | |
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| 49.4297 | 26000 | 0.0013 | |
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### Framework Versions |
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- Python: 3.8.5 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.1.1+cu121 |
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- Accelerate: 0.32.1 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@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", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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