Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model_int8.onnx.bak_pre_caza_2026-06-20 filter=lfs diff=lfs merge=lfs -text
+unigram.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,386 @@
+---
+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
+    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'
+  - 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
+  - 'WhatsApp messaging: send a message, REPLY/answer a message, open a chat, mandar/responder/contestar
+    un mensaje por WhatsApp, respondele/contestale a una persona, escribir a alguien
+    en wsp, decirle algo a alguien en whatsapp, mensaje de texto'
+- source_sentence: abre configuración y entra a Bluetooth
+  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
+    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
+    en discord, ensordecer discord, barra de favoritos, marcador, abrir un navegador
+    y clickear un favorito'
+- source_sentence: list my open tabs
+  sentences:
+  - 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
+    canción o video, poner música, spiel ein Lied ab, metti una canzone, toca uma
+    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
+    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
+- **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
+- **Output Dimensionality:** 384 dimensions
+- **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': 128, '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 = [
+    '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)
+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
+* 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                                                                                                                                                                                                                                                                                                                                                                            |
+  |:-------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <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
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

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modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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special_tokens_map.json

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tokenizer_config.json

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