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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 model finetuned from 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
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]

Training Details

Training Dataset

Unnamed Dataset

Size: 11,808 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 3 tokens
mean: 10.0 tokens
max: 46 tokens

min: 9 tokens
mean: 71.13 tokens
max: 128 tokens

	sentence_0	sentence_1
type	string	string
details	min: 3 tokens mean: 10.0 tokens max: 46 tokens	min: 9 tokens mean: 71.13 tokens max: 128 tokens

Samples:

sentence_0	sentence_1
`abre Notepad y escribe hola con GUI`	`screenshot, click, type text, hotkeys, scroll, drag with mouse`
`schließ den Editor`	`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`
`Mets le volume à 25 pour cent`	`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`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "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

Click to expand

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

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

@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

@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}
}