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adriansanz
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ST-tramits-SB-001-5ep

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:2372
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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SentenceTransformer based on BAAI/bge-m3

This is a sentence-transformers model finetuned from BAAI/bge-m3 on the json dataset. 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 tokens
  • Similarity Function: Cosine Similarity
  • Training Dataset:
    • json

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("adriansanz/ST-tramits-SB-001-5ep")
# Run inference
sentences = [
    'Descripció. Retorna en format JSON adequat',
    "Quin és el contingut de l'annex específic?",
    "Què passa amb l'habitatge?",
]
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]

Evaluation

Metrics

Information Retrieval

Metric Value
cosine_accuracy@1 0.3322
cosine_accuracy@3 0.5902
cosine_accuracy@5 0.6998
cosine_accuracy@10 0.8094
cosine_precision@1 0.3322
cosine_precision@3 0.1967
cosine_precision@5 0.14
cosine_precision@10 0.0809
cosine_recall@1 0.3322
cosine_recall@3 0.5902
cosine_recall@5 0.6998
cosine_recall@10 0.8094
cosine_ndcg@10 0.5626
cosine_mrr@10 0.4843
cosine_map@100 0.4924

Information Retrieval

Metric Value
cosine_accuracy@1 0.3406
cosine_accuracy@3 0.5767
cosine_accuracy@5 0.6981
cosine_accuracy@10 0.8162
cosine_precision@1 0.3406
cosine_precision@3 0.1922
cosine_precision@5 0.1396
cosine_precision@10 0.0816
cosine_recall@1 0.3406
cosine_recall@3 0.5767
cosine_recall@5 0.6981
cosine_recall@10 0.8162
cosine_ndcg@10 0.5661
cosine_mrr@10 0.4872
cosine_map@100 0.4952

Information Retrieval

Metric Value
cosine_accuracy@1 0.3305
cosine_accuracy@3 0.5801
cosine_accuracy@5 0.6948
cosine_accuracy@10 0.8162
cosine_precision@1 0.3305
cosine_precision@3 0.1934
cosine_precision@5 0.139
cosine_precision@10 0.0816
cosine_recall@1 0.3305
cosine_recall@3 0.5801
cosine_recall@5 0.6948
cosine_recall@10 0.8162
cosine_ndcg@10 0.563
cosine_mrr@10 0.483
cosine_map@100 0.4908

Information Retrieval

Metric Value
cosine_accuracy@1 0.3288
cosine_accuracy@3 0.5885
cosine_accuracy@5 0.7015
cosine_accuracy@10 0.8094
cosine_precision@1 0.3288
cosine_precision@3 0.1962
cosine_precision@5 0.1403
cosine_precision@10 0.0809
cosine_recall@1 0.3288
cosine_recall@3 0.5885
cosine_recall@5 0.7015
cosine_recall@10 0.8094
cosine_ndcg@10 0.5626
cosine_mrr@10 0.4842
cosine_map@100 0.492

Information Retrieval

Metric Value
cosine_accuracy@1 0.3474
cosine_accuracy@3 0.5818
cosine_accuracy@5 0.6998
cosine_accuracy@10 0.8061
cosine_precision@1 0.3474
cosine_precision@3 0.1939
cosine_precision@5 0.14
cosine_precision@10 0.0806
cosine_recall@1 0.3474
cosine_recall@3 0.5818
cosine_recall@5 0.6998
cosine_recall@10 0.8061
cosine_ndcg@10 0.5654
cosine_mrr@10 0.4894
cosine_map@100 0.4973

Information Retrieval

Metric Value
cosine_accuracy@1 0.2917
cosine_accuracy@3 0.5683
cosine_accuracy@5 0.6644
cosine_accuracy@10 0.7875
cosine_precision@1 0.2917
cosine_precision@3 0.1894
cosine_precision@5 0.1329
cosine_precision@10 0.0788
cosine_recall@1 0.2917
cosine_recall@3 0.5683
cosine_recall@5 0.6644
cosine_recall@10 0.7875
cosine_ndcg@10 0.532
cosine_mrr@10 0.4512
cosine_map@100 0.4595

Training Details

Training Dataset

json

  • Dataset: json
  • Size: 2,372 training samples
  • Columns: positive and anchor
  • Approximate statistics based on the first 1000 samples:
    positive anchor
    type string string
    details
    • min: 3 tokens
    • mean: 35.12 tokens
    • max: 166 tokens
    • min: 8 tokens
    • mean: 19.49 tokens
    • max: 47 tokens
  • Samples:
    positive anchor
    Comunicar la variació d'alguna de les següents dades del Padró Municipal d'Habitants: Nom, Cognoms, Data de naixement, DNI, Passaport, Número de permís de residència (NIE), Sexe, Municipi i/o província de naixement, Nacionalitat, Titulació acadèmica. Quin és l'objectiu del canvi de dades personals en el Padró Municipal d'Habitants?
    EN QUÈ CONSISTEIX: Tramitar la sol·licitud de matrimoni civil a l'Ajuntament. Què és el matrimoni civil a l'Ajuntament de Sant Boi de Llobregat?
    En domiciliar el pagament de tributs municipals en entitats bancàries. Quin és el benefici de domiciliar el pagament de tributs?
  • Loss: MatryoshkaLoss with these parameters:
    {
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        1024,
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: epoch
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • gradient_accumulation_steps: 16
  • learning_rate: 2e-05
  • num_train_epochs: 5
  • lr_scheduler_type: cosine
  • warmup_ratio: 0.2
  • bf16: True
  • tf32: True
  • load_best_model_at_end: True
  • optim: adamw_torch_fused
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: epoch
  • 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: 16
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 2e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 5
  • max_steps: -1
  • lr_scheduler_type: cosine
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.2
  • 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: True
  • fp16: False
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: True
  • 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: True
  • 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_fused
  • 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
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Epoch Step Training Loss dim_1024_cosine_map@100 dim_768_cosine_map@100 dim_512_cosine_map@100 dim_256_cosine_map@100 dim_128_cosine_map@100 dim_64_cosine_map@100
0.9664 9 - 0.4730 0.4766 0.4640 0.4612 0.4456 0.4083
1.0738 10 2.6023 - - - - - -
1.9329 18 - 0.4951 0.4966 0.4977 0.4773 0.4849 0.4501
2.1477 20 0.974 - - - - - -
2.8993 27 - 0.4891 0.4973 0.4941 0.4867 0.4925 0.4684
3.2215 30 0.408 - - - - - -
3.9732 37 - 0.4944 0.4998 0.4931 0.4991 0.4974 0.4616
4.2953 40 0.2718 - - - - - -
4.8322 45 - 0.4924 0.4952 0.4908 0.4920 0.4973 0.4595
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.2.0
  • Transformers: 4.44.2
  • PyTorch: 2.4.1+cu121
  • Accelerate: 1.1.0.dev0
  • Datasets: 3.0.1
  • 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",
}

MatryoshkaLoss 

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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}
}
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