Hugging Face's logo

qygoh
/
ilo-embedding-model

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:121
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Model card Files
xet
 Community
ilo-embedding-model / README.md
qygoh's picture
qygoh
Add files using upload-large-folder tool
0af5c22 verified
preview code
|
raw
history blame contribute delete
13.2 kB
metadata 
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:121
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-m3
widget:
  - source_sentence: Kasano a mausar ti online a panag-apply iti tulong dagiti Golden Citizens?
    sentences:
      - >-
        Ania dagiti addang a mangaplikar iti tulong kadagiti umili babaen ti
        online system?
      - Ania ti pamay-an a nalaklaka a mangasaba iti tulong kadagiti umili?
      - >-
        Ania dagiti addang a mabalin nga aramiden tapno maaddaan iti status ti
        binulan a sueldo iti agdama a tawen?
  - source_sentence: Ania dagiti kasapulan tapno agpatulong ni Sara a saan a mapan iti counter?
    sentences:
      - kualipikasion ken ti bayad ket Agosto 2026
      - >-
        Ania dagiti kasapulan tapno agpatulong ni Sara a saan a mapan iti
        counter?
      - ti kinatalged ti ekonomia ti MADANI.
  - source_sentence: >-
      JPM) agingga iti 31 Oktubre 2025 wenno iti petsa ti regular a
      pannakasukimat.
    sentences:
      - >-
        palso a link ken palso a napukaw a pamay-an, ken agdaydayaw laeng iti
        opisial a portal ti STR
      - >-
        JPM) agingga iti 31 Oktubre 2025 wenno iti petsa ti regular a
        pannakasukimat.
      - Ania dagiti nalaklaka a pamay-an a mangikabil iti income certificate?
  - source_sentence: >-
      Kasano a mausar ti maysa a dokumento a mangikeddeng no ania ti maited iti
      agdama a tawen?
    sentences:
      - Ania ti pamay-an a nalaklaka a mangasaba ken Sara?
      - >-
        Ania dagiti kasapulan tapno agpatulongka iti SARA a saan a mapan iti
        counter?
      - >-
        Ania dagiti addang a mabalin nga aramiden tapno maited ti
        pannakabalbaliw ti impormasion ti agkedked iti agdama a tawen?
  - source_sentence: Kasano a maaddaan iti pamilia a mangapektar iti biagda iti Internet?
    sentences:
      - >-
        Ania dagiti addang a mabalin nga aramiden tapno maaddaan iti tulong iti
        edukasion ti ubing iti agdama a tawen?
      - >-
        Ania dagiti addang a mabalin nga aramiden tapno mausar ti online a
        sistema ti pamilia?
      - Ania dagiti kasapulan tapno nalaklaka ti agpatulong iti SARA?
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on BAAI/bge-m3

This is a sentence-transformers model finetuned from BAAI/bge-m3. It maps sentences & paragraphs to a 1024-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: BAAI/bge-m3
  • Maximum Sequence Length: 8192 tokens
  • Output Dimensionality: 1024 dimensions
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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 = [
    'Kasano a maaddaan iti pamilia a mangapektar iti biagda iti Internet?',
    'Ania dagiti addang a mabalin nga aramiden tapno mausar ti online a sistema ti pamilia?',
    'Ania dagiti kasapulan tapno nalaklaka ti agpatulong iti SARA?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

  • Size: 121 training samples
  • Columns: sentence_0 and sentence_1
  • Approximate statistics based on the first 121 samples:
    sentence_0 sentence_1
    type string string
    details
    • min: 13 tokens
    • mean: 24.36 tokens
    • max: 45 tokens
    • min: 12 tokens
    • mean: 26.77 tokens
    • max: 41 tokens
  • Samples:
    sentence_0 sentence_1
    A: Saan. Ti 2026 a SARA ket automatiko a naibatay kadagiti datos ti Agricultural Poverty. A: Saan. Ti SARA 2026 ket automatiko a naibatay kadagiti datos ti Agricultural Poverty.
    ti mangpasayaat iti ekonomia dagiti marigrigat ken mangitandudo kadagiti prinsipio ti panangpabileg iti ekonomia dagiti napanglaw ken panangsuporta kadagiti prinsipio
    Kasano a masiguradotayo a dagus ken umiso ti panangasaba iti STR? Ania ti mabalin a pamay-an tapno nalaklaka ti agpatulong iti STR?
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • num_train_epochs: 1
  • 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: 16
  • per_device_eval_batch_size: 16
  • 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: 1
  • 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: None
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • include_for_metrics: []
  • 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
  • use_liger_kernel: False
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: round_robin

Framework Versions

  • Python: 3.13.9
  • Sentence Transformers: 3.3.1
  • Transformers: 4.47.1
  • PyTorch: 2.6.0+cpu
  • Accelerate: 1.2.1
  • Datasets: 4.7.0
  • Tokenizers: 0.21.4

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}
}