| ---
|
| tags:
|
| - sentence-transformers
|
| - sentence-similarity
|
| - feature-extraction
|
| - generated_from_trainer
|
| - dataset_size:11808
|
| - loss:MultipleNegativesRankingLoss
|
| base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
|
| widget:
|
| - source_sentence: abre o Netflix
|
| sentences:
|
| - 'open a website, URL or web address in a browser; abrir una pagina web, abrir
|
| github o un sitio, abrir una direccion como python.org o localhost, navegar a
|
| un enlace, open a link in the browser. TAMBIEN: buscar o comprar un producto o
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| juego en una TIENDA WEB (Instant Gaming, Steam store online, Epic Games, GOG,
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| Amazon): ir al sitio de la tienda y buscar ahi el juego; find or buy a game on
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| an online store website, go to the store site and search for the product'
|
| - audio volume, raise or lower the sound volume, turn it up or down (subir/bajar
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| el volumen, lauter/leiser machen, alza/abbassa il volume, aumenta/diminui o volume,
|
| monter/baisser le volume); mute and unmute the sound (silenciar, desmutear, stummschalten,
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| ton an, couper/rétablir le son, silenciar/reativar o som), system sound, media
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| keys, audio devices listing
|
| - 'WhatsApp messaging: send a message, REPLY/answer a message, open a chat, mandar/responder/contestar
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| un mensaje por WhatsApp, respondele/contestale a una persona, escribir a alguien
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| en wsp, decirle algo a alguien en whatsapp, mensaje de texto'
|
| - source_sentence: abre configuración y entra a Bluetooth
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| sentences:
|
| - create a local or cloud reminder
|
| - 'open a website, URL or web address in a browser; abrir una pagina web, abrir
|
| github o un sitio, abrir una direccion como python.org o localhost, navegar a
|
| un enlace, open a link in the browser. TAMBIEN: buscar o comprar un producto o
|
| juego en una TIENDA WEB (Instant Gaming, Steam store online, Epic Games, GOG,
|
| Amazon): ir al sitio de la tienda y buscar ahi el juego; find or buy a game on
|
| an online store website, go to the store site and search for the product'
|
| - 'operate inside an open app: Discord text channel, voice channel, send messages
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| in Discord/Slack/Teams, mute or unmute Discord microphone, deafen or undeafen
|
| Discord, leave a voice call, open or focus a browser, navigate inside a browser/app,
|
| click a bookmark/favorite/link/result/named control, chain browser actions to
|
| reach a goal. canal de voz discord, canal de texto discord, mutea mi microfono
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| en discord, ensordecer discord, barra de favoritos, marcador, abrir un navegador
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| y clickear un favorito'
|
| - source_sentence: list my open tabs
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| sentences:
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| - list windows, focus, close, minimize, maximize, move, resize
|
| - create automation routines with manual, cron, or on-app-open triggers
|
| - play music or a video, pause, stop, next, previous, now playing; reproducir una
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| canción o video, poner música, spiel ein Lied ab, metti una canzone, toca uma
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| música, mets une chanson, abspielen
|
| - source_sentence: conectate al wifi de casa
|
| sentences:
|
| - abrir, cerrar o encontrar un programa o juego ya instalado en esta computadora,
|
| desde el menú de inicio; open, close or find an installed desktop application
|
| on this PC by name
|
| - 'drive a real browser via Playwright: tabs, fill forms, click, extract text'
|
| - connect to a WiFi network / join wifi / switch wifi network, Bluetooth, display,
|
| power plans, system settings — conectar/conectarse a una red WiFi
|
| - source_sentence: andá a la página de YouTube
|
| sentences:
|
| - 'open a website, URL or web address in a browser; abrir una pagina web, abrir
|
| github o un sitio, abrir una direccion como python.org o localhost, navegar a
|
| un enlace, open a link in the browser. TAMBIEN: buscar o comprar un producto o
|
| juego en una TIENDA WEB (Instant Gaming, Steam store online, Epic Games, GOG,
|
| Amazon): ir al sitio de la tienda y buscar ahi el juego; find or buy a game on
|
| an online store website, go to the store site and search for the product'
|
| - Steam game library, Steam store, launch installed Steam games
|
| - 'system info, processes and power: current time and date, qué hora es, qué fecha
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| es hoy; list running processes, kill or force-close a process, taskkill, mata
|
| procesos colgados, lista procesos, cierra un proceso; CPU RAM GPU disk; how much
|
| memory/RAM do I have, free memory, memory usage, RAM usage, cuánta memoria tengo,
|
| cuánta memoria RAM libre tengo, uso de memoria, memoria del sistema, quanta memória
|
| tenho, quanto di memoria ho, wie viel Arbeitsspeicher habe ich, combien de mémoire
|
| RAM; SCREEN/display brightness up/down — subir/bajar el brillo de la pantalla,
|
| atenuar la pantalla, set screen brightness; battery level, shutdown restart sleep'
|
| pipeline_tag: sentence-similarity
|
| library_name: sentence-transformers
|
| ---
|
|
|
| # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
|
|
|
| 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.
|
|
|
| ## Model Details
|
|
|
| ### Model Description
|
| - **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 e8f8c211226b894fcb81acc59f3b34ba3efd5f42 -->
|
| - **Maximum Sequence Length:** 128 tokens
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| - **Output Dimensionality:** 384 dimensions
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| - **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)
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| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
|
|
| ### Full Model Architecture
|
|
|
| ```
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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})
|
| )
|
| ```
|
|
|
| ## Usage
|
|
|
| ### Direct Usage (Sentence Transformers)
|
|
|
| First install the Sentence Transformers library:
|
|
|
| ```bash
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| pip install -U sentence-transformers
|
| ```
|
|
|
| Then you can load this model and run inference.
|
| ```python
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| from sentence_transformers import SentenceTransformer
|
|
|
| # Download from the 🤗 Hub
|
| model = SentenceTransformer("sentence_transformers_model_id")
|
| # Run inference
|
| sentences = [
|
| 'andá a la página de YouTube',
|
| 'open a website, URL or web address in a browser; abrir una pagina web, abrir github o un sitio, abrir una direccion como python.org o localhost, navegar a un enlace, open a link in the browser. TAMBIEN: buscar o comprar un producto o juego en una TIENDA WEB (Instant Gaming, Steam store online, Epic Games, GOG, Amazon): ir al sitio de la tienda y buscar ahi el juego; find or buy a game on an online store website, go to the store site and search for the product',
|
| 'Steam game library, Steam store, launch installed Steam games',
|
| ]
|
| embeddings = model.encode(sentences)
|
| print(embeddings.shape)
|
| # [3, 384]
|
|
|
| # Get the similarity scores for the embeddings
|
| similarities = model.similarity(embeddings, embeddings)
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| 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: 11,808 training samples
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| * 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: 3 tokens</li><li>mean: 10.0 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 71.13 tokens</li><li>max: 128 tokens</li></ul> |
|
| * Samples:
|
| | sentence_0 | sentence_1 |
|
| |:-------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| | <code>abre Notepad y escribe hola con GUI</code> | <code>screenshot, click, type text, hotkeys, scroll, drag with mouse</code> |
|
| | <code>schließ den Editor</code> | <code>abrir, cerrar o encontrar un programa o juego ya instalado en esta computadora, desde el menú de inicio; open, close or find an installed desktop application on this PC by name</code> |
|
| | <code>Mets le volume à 25 pour cent</code> | <code>audio volume, raise or lower the sound volume, turn it up or down (subir/bajar el volumen, lauter/leiser machen, alza/abbassa il volume, aumenta/diminui o volume, monter/baisser le volume); mute and unmute the sound (silenciar, desmutear, stummschalten, ton an, couper/rétablir le son, silenciar/reativar o som), system sound, media keys, audio devices listing</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`: 64
|
| - `per_device_eval_batch_size`: 64
|
| - `num_train_epochs`: 2
|
| - `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`: 64
|
| - `per_device_eval_batch_size`: 64
|
| - `per_gpu_train_batch_size`: None
|
| - `per_gpu_eval_batch_size`: None
|
| - `gradient_accumulation_steps`: 1
|
| - `eval_accumulation_steps`: None
|
| - `torch_empty_cache_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`: 2
|
| - `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
|
| - `eval_use_gather_object`: False
|
| - `prompts`: None
|
| - `batch_sampler`: batch_sampler
|
| - `multi_dataset_batch_sampler`: round_robin
|
|
|
| </details>
|
|
|
| ### Framework Versions
|
| - Python: 3.11.15
|
| - Sentence Transformers: 3.3.1
|
| - Transformers: 4.44.2
|
| - PyTorch: 2.6.0+cu124
|
| - Accelerate: 1.13.0
|
| - Datasets: 2.21.0
|
| - 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
|
|
|
| *Clearly define terms in order to be accessible across audiences.*
|
| -->
|
|
|
| <!--
|
| ## Model Card Authors
|
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| -->
|
|
|
| <!--
|
| ## Model Card Contact
|
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|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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