| | --- |
| | base_model: Supabase/gte-small |
| | datasets: [] |
| | language: [] |
| | library_name: sentence-transformers |
| | pipeline_tag: sentence-similarity |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:68 |
| | - loss:MultipleNegativesRankingLoss |
| | widget: |
| | - source_sentence: Pollo al curri rojo |
| | sentences: |
| | - Vino blanco seco, ligero, refrescante, delicado y sauve. Con sabor ligero y poco |
| | denso, acostumbran a ser vinos jóvenes. Con aromas a cítricos, manzana verde, |
| | melocotón, piña e hinojo. |
| | - Vinos rosados envejecidos en barrica, y también los vinos rosados elaborados a |
| | partir de Cabernet, Merlot o Syrah. Vinos rosados redondos, afrutados, de color |
| | intenso y sabor potente y sabroso. Con maceración de la piel. |
| | - 'Vino blanco afrutado de medio cuerpo, con aromas a melocotón, piña, uva, fruta |
| | de la pasión, queroseno y flores. Ejemplos de variedades: los Verdejo de Rueda, |
| | Valencia Moscatell, los Malvasía de Canarias, los Riesling de Alsacia i Alemania. |
| | los Gerwüztraminer.' |
| | - source_sentence: 'Salmón cocinado a baja temperatura en 3 pimientas ' |
| | sentences: |
| | - Vinos tintos afrutados, jugosos y desenfadados. Con aromas a frutos rojos, lácticos. |
| | pimienta, ciruela y mermeladas. Son vinos sencillos y amables, golosos y frescos |
| | a partes iguales. |
| | - Vino blanco seco, ligero, refrescante, delicado y sauve. Con sabor ligero y poco |
| | denso, acostumbran a ser vinos jóvenes. Con aromas a cítricos, manzana verde, |
| | melocotón, piña e hinojo. |
| | - Blancos secos y tintos ligeros |
| | - source_sentence: Nuggets de pollo rebozados en tempura |
| | sentences: |
| | - Vino blanco joven con buena acidez o un vino rosado afrutado. |
| | - Vino blanco seco, ligero, refrescante, delicado y sauve. Con sabor ligero y poco |
| | denso, acostumbran a ser vinos jóvenes. Con aromas a cítricos, manzana verde, |
| | melocotón, piña e hinojo. |
| | - ' Vinos tintos ligeros con mucha acidez y poco volumen en boca, con notas de fruta |
| | roja muy fresca, sin presencia de taninos; normalmente con notas verdes. ejemplos: |
| | mencia, gammay, pinot noir.' |
| | - source_sentence: Patatas bravas |
| | sentences: |
| | - vinos dulces que son afrutados y muy aromáticos. De gusto dulce pero no empalagoso.también |
| | vino fortificados de vinos jóvenes. Con aromas a uva, rosas, pasas, lichi, higos |
| | y caramelo. |
| | - ' Vinos tintos ligeros con mucha acidez y poco volumen en boca, con notas de fruta |
| | roja muy fresca, sin presencia de taninos; normalmente con notas verdes. ejemplos: |
| | mencia, gammay, pinot noir. O incluso vinos elaborados a partir de Cabernet, Merlot |
| | o Syrah. Vinos rosados redondos, afrutados, de color intenso y sabor potente y |
| | sabroso. Con maceración de la piel. ' |
| | - Vino blanco con notas cítricas y acidez refrescante. |
| | - source_sentence: 'Chipirones a la plancha con patata ' |
| | sentences: |
| | - Vino blanco seco, ligero, refrescante, delicado y sauve. Con sabor ligero y poco |
| | denso, acostumbran a ser vinos jóvenes. Con aromas a cítricos, manzana verde, |
| | melocotón, piña e hinojo. O también vinos rosados ligeros, referescantes, delicados |
| | y de color pálido. En boca son ligeros y de sabor delicado. Con aromas a fruta |
| | roja silvestre, cítricos y herbáceos. |
| | - Vinos tintos afrutados, jugosos y desenfadados. Con aromas a frutos rojos, lácticos. |
| | pimienta, ciruela y mermeladas. Son vinos sencillos y amables, golosos y frescos |
| | a partes iguales. |
| | - 'Vinos blancos con cuerpo, amplios y sabrosos. En boca potentes, untuosos y densos |
| | fruto del paso por barrica. Vinos blancos con intensidad aromática alta y con |
| | aromas a manzana Golden, mantequilla, pan tostado, vainilla, frutos secos. Ejemplo: |
| | Chardonnay, Garnacha blanca, Viura de Rioja.' |
| | --- |
| | |
| | # SentenceTransformer based on Supabase/gte-small |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Supabase/gte-small](https://huggingface.co/Supabase/gte-small). 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. |
| |
|
| | ## Model Details |
| |
|
| | ### Model Description |
| | - **Model Type:** Sentence Transformer |
| | - **Base model:** [Supabase/gte-small](https://huggingface.co/Supabase/gte-small) <!-- at revision 93b36ff09519291b77d6000d2e86bd8565378086 --> |
| | - **Maximum Sequence Length:** 512 tokens |
| | - **Output Dimensionality:** 384 tokens |
| | - **Similarity Function:** Cosine Similarity |
| | <!-- - **Training Dataset:** Unknown --> |
| | <!-- - **Language:** Unknown --> |
| | <!-- - **License:** Unknown --> |
| |
|
| | ### Model Sources |
| |
|
| | - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| | - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| | - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
| |
|
| | ### Full Model Architecture |
| |
|
| | ``` |
| | SentenceTransformer( |
| | (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
| | (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}) |
| | ) |
| | ``` |
| |
|
| | ## Usage |
| |
|
| | ### Direct Usage (Sentence Transformers) |
| |
|
| | First install the Sentence Transformers library: |
| |
|
| | ```bash |
| | pip install -U sentence-transformers |
| | ``` |
| |
|
| | Then you can load this model and run inference. |
| | ```python |
| | from sentence_transformers import SentenceTransformer |
| | |
| | # Download from the 🤗 Hub |
| | model = SentenceTransformer("sentence_transformers_model_id") |
| | # Run inference |
| | sentences = [ |
| | 'Chipirones a la plancha con patata ', |
| | 'Vino blanco seco, ligero, refrescante, delicado y sauve. Con sabor ligero y poco denso, acostumbran a ser vinos jóvenes. Con aromas a cítricos, manzana verde, melocotón, piña e hinojo. O también vinos rosados ligeros, referescantes, delicados y de color pálido. En boca son ligeros y de sabor delicado. Con aromas a fruta roja silvestre, cítricos y herbáceos.', |
| | 'Vinos blancos con cuerpo, amplios y sabrosos. En boca potentes, untuosos y densos fruto del paso por barrica. Vinos blancos con intensidad aromática alta y con aromas a manzana Golden, mantequilla, pan tostado, vainilla, frutos secos. Ejemplo: Chardonnay, Garnacha blanca, Viura de Rioja.', |
| | ] |
| | embeddings = model.encode(sentences) |
| | print(embeddings.shape) |
| | # [3, 384] |
| | |
| | # Get the similarity scores for the embeddings |
| | similarities = model.similarity(embeddings, embeddings) |
| | print(similarities.shape) |
| | # [3, 3] |
| | ``` |
| |
|
| | <!-- |
| | ### Direct Usage (Transformers) |
| |
|
| | <details><summary>Click to see the direct usage in Transformers</summary> |
| |
|
| | </details> |
| | --> |
| |
|
| | <!-- |
| | ### Downstream Usage (Sentence Transformers) |
| |
|
| | You can finetune this model on your own dataset. |
| |
|
| | <details><summary>Click to expand</summary> |
| |
|
| | </details> |
| | --> |
| |
|
| | <!-- |
| | ### Out-of-Scope Use |
| |
|
| | *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| | --> |
| |
|
| | <!-- |
| | ## Bias, Risks and Limitations |
| |
|
| | *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| | --> |
| |
|
| | <!-- |
| | ### Recommendations |
| |
|
| | *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| | --> |
| |
|
| | ## Training Details |
| |
|
| | ### Training Dataset |
| |
|
| | #### Unnamed Dataset |
| |
|
| |
|
| | * Size: 68 training samples |
| | * Columns: <code>sentence_0</code> and <code>sentence_1</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | sentence_0 | sentence_1 | |
| | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
| | | type | string | string | |
| | | details | <ul><li>min: 4 tokens</li><li>mean: 16.46 tokens</li><li>max: 82 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 64.46 tokens</li><li>max: 178 tokens</li></ul> | |
| | * Samples: |
| | | sentence_0 | sentence_1 | |
| | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | <code>Rollito de primavera de carne</code> | <code>Vino tinto joven y afrutado o un vino blanco joven con buena acidez.</code> | |
| | | <code>Platos contundentes como carnes de caza: jabalí, pichón, etc. También carnes rojas como ternera, cordero, etc. Guisos y platos de cuchara con embutidos y carnes rojas.</code> | <code>Vino tinto con mucha intensidad y potencia, con notas a fruta tinta madura, notas a madera, notas a pimienta negra, a café, cacao. Con presencia de taninos bien integrados fruto del contacto con las pieles durante un largo período. Son sabrosos, corpulentos, impactantes. </code> | |
| | | <code>Patatas bravas</code> | <code> Vinos tintos ligeros con mucha acidez y poco volumen en boca, con notas de fruta roja muy fresca, sin presencia de taninos; normalmente con notas verdes. ejemplos: mencia, gammay, pinot noir. O incluso vinos elaborados a partir de Cabernet, Merlot o Syrah. Vinos rosados redondos, afrutados, de color intenso y sabor potente y sabroso. Con maceración de la piel. </code> | |
| | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| | ```json |
| | { |
| | "scale": 20.0, |
| | "similarity_fct": "cos_sim" |
| | } |
| | ``` |
| |
|
| | ### Training Hyperparameters |
| | #### Non-Default Hyperparameters |
| |
|
| | - `per_device_train_batch_size`: 4 |
| | - `per_device_eval_batch_size`: 4 |
| | - `num_train_epochs`: 30 |
| | - `multi_dataset_batch_sampler`: round_robin |
| | |
| | #### All Hyperparameters |
| | <details><summary>Click to expand</summary> |
| | |
| | - `overwrite_output_dir`: False |
| | - `do_predict`: False |
| | - `eval_strategy`: no |
| | - `prediction_loss_only`: True |
| | - `per_device_train_batch_size`: 4 |
| | - `per_device_eval_batch_size`: 4 |
| | - `per_gpu_train_batch_size`: None |
| | - `per_gpu_eval_batch_size`: None |
| | - `gradient_accumulation_steps`: 1 |
| | - `eval_accumulation_steps`: None |
| | - `learning_rate`: 5e-05 |
| | - `weight_decay`: 0.0 |
| | - `adam_beta1`: 0.9 |
| | - `adam_beta2`: 0.999 |
| | - `adam_epsilon`: 1e-08 |
| | - `max_grad_norm`: 1 |
| | - `num_train_epochs`: 30 |
| | - `max_steps`: -1 |
| | - `lr_scheduler_type`: linear |
| | - `lr_scheduler_kwargs`: {} |
| | - `warmup_ratio`: 0.0 |
| | - `warmup_steps`: 0 |
| | - `log_level`: passive |
| | - `log_level_replica`: warning |
| | - `log_on_each_node`: True |
| | - `logging_nan_inf_filter`: True |
| | - `save_safetensors`: True |
| | - `save_on_each_node`: False |
| | - `save_only_model`: False |
| | - `restore_callback_states_from_checkpoint`: False |
| | - `no_cuda`: False |
| | - `use_cpu`: False |
| | - `use_mps_device`: False |
| | - `seed`: 42 |
| | - `data_seed`: None |
| | - `jit_mode_eval`: False |
| | - `use_ipex`: False |
| | - `bf16`: False |
| | - `fp16`: False |
| | - `fp16_opt_level`: O1 |
| | - `half_precision_backend`: auto |
| | - `bf16_full_eval`: False |
| | - `fp16_full_eval`: False |
| | - `tf32`: None |
| | - `local_rank`: 0 |
| | - `ddp_backend`: None |
| | - `tpu_num_cores`: None |
| | - `tpu_metrics_debug`: False |
| | - `debug`: [] |
| | - `dataloader_drop_last`: False |
| | - `dataloader_num_workers`: 0 |
| | - `dataloader_prefetch_factor`: None |
| | - `past_index`: -1 |
| | - `disable_tqdm`: False |
| | - `remove_unused_columns`: True |
| | - `label_names`: None |
| | - `load_best_model_at_end`: False |
| | - `ignore_data_skip`: False |
| | - `fsdp`: [] |
| | - `fsdp_min_num_params`: 0 |
| | - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| | - `fsdp_transformer_layer_cls_to_wrap`: None |
| | - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| | - `deepspeed`: None |
| | - `label_smoothing_factor`: 0.0 |
| | - `optim`: adamw_torch |
| | - `optim_args`: None |
| | - `adafactor`: False |
| | - `group_by_length`: False |
| | - `length_column_name`: length |
| | - `ddp_find_unused_parameters`: None |
| | - `ddp_bucket_cap_mb`: None |
| | - `ddp_broadcast_buffers`: False |
| | - `dataloader_pin_memory`: True |
| | - `dataloader_persistent_workers`: False |
| | - `skip_memory_metrics`: True |
| | - `use_legacy_prediction_loop`: False |
| | - `push_to_hub`: False |
| | - `resume_from_checkpoint`: None |
| | - `hub_model_id`: None |
| | - `hub_strategy`: every_save |
| | - `hub_private_repo`: False |
| | - `hub_always_push`: False |
| | - `gradient_checkpointing`: False |
| | - `gradient_checkpointing_kwargs`: None |
| | - `include_inputs_for_metrics`: False |
| | - `eval_do_concat_batches`: True |
| | - `fp16_backend`: auto |
| | - `push_to_hub_model_id`: None |
| | - `push_to_hub_organization`: None |
| | - `mp_parameters`: |
| | - `auto_find_batch_size`: False |
| | - `full_determinism`: False |
| | - `torchdynamo`: None |
| | - `ray_scope`: last |
| | - `ddp_timeout`: 1800 |
| | - `torch_compile`: False |
| | - `torch_compile_backend`: None |
| | - `torch_compile_mode`: None |
| | - `dispatch_batches`: None |
| | - `split_batches`: None |
| | - `include_tokens_per_second`: False |
| | - `include_num_input_tokens_seen`: False |
| | - `neftune_noise_alpha`: None |
| | - `optim_target_modules`: None |
| | - `batch_eval_metrics`: False |
| | - `eval_on_start`: False |
| | - `batch_sampler`: batch_sampler |
| | - `multi_dataset_batch_sampler`: round_robin |
| | |
| | </details> |
| | |
| | ### Training Logs |
| | | Epoch | Step | Training Loss | |
| | |:-------:|:----:|:-------------:| |
| | | 29.4118 | 500 | 0.2948 | |
| | |
| | |
| | ### Framework Versions |
| | - Python: 3.10.12 |
| | - Sentence Transformers: 3.0.1 |
| | - Transformers: 4.42.4 |
| | - PyTorch: 2.3.1+cu121 |
| | - Accelerate: 0.32.1 |
| | - Datasets: 2.20.0 |
| | - Tokenizers: 0.19.1 |
| | |
| | ## Citation |
| | |
| | ### BibTeX |
| | |
| | #### Sentence Transformers |
| | ```bibtex |
| | @inproceedings{reimers-2019-sentence-bert, |
| | title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
| | author = "Reimers, Nils and Gurevych, Iryna", |
| | booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
| | month = "11", |
| | year = "2019", |
| | publisher = "Association for Computational Linguistics", |
| | url = "https://arxiv.org/abs/1908.10084", |
| | } |
| | ``` |
| | |
| | #### 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} |
| | } |
| | ``` |
| | |
| | <!-- |
| | ## Glossary |
| | |
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