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 tokens
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': 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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("adriansanz/sitgrsBAAIbge-m3-290824")
# Run inference
sentences = [
    'Les entitats inscrites en el Registre resten obligades a comunicar a l’Ajuntament qualsevol modificació en les seves dades registrals, podent sol·licitar la seva cancel·lació o comunicant la seva dissolució.',
    "Quin és el procediment per cancel·lar la inscripció d'una entitat al Registre municipal d'entitats?",
    'Quin és el paper de les entitats de protecció dels animals en la gestió de les colònies urbanes felines?',
]
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

Dataset: dim_1024
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0862
cosine_accuracy@3	0.2155
cosine_accuracy@5	0.3276
cosine_accuracy@10	0.5108
cosine_precision@1	0.0862
cosine_precision@3	0.0718
cosine_precision@5	0.0655
cosine_precision@10	0.0511
cosine_recall@1	0.0862
cosine_recall@3	0.2155
cosine_recall@5	0.3276
cosine_recall@10	0.5108
cosine_ndcg@10	0.264
cosine_mrr@10	0.1897
cosine_map@100	0.2151

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0841
cosine_accuracy@3	0.2091
cosine_accuracy@5	0.319
cosine_accuracy@10	0.5
cosine_precision@1	0.0841
cosine_precision@3	0.0697
cosine_precision@5	0.0638
cosine_precision@10	0.05
cosine_recall@1	0.0841
cosine_recall@3	0.2091
cosine_recall@5	0.319
cosine_recall@10	0.5
cosine_ndcg@10	0.2595
cosine_mrr@10	0.1867
cosine_map@100	0.2132

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0862
cosine_accuracy@3	0.2112
cosine_accuracy@5	0.3211
cosine_accuracy@10	0.5129
cosine_precision@1	0.0862
cosine_precision@3	0.0704
cosine_precision@5	0.0642
cosine_precision@10	0.0513
cosine_recall@1	0.0862
cosine_recall@3	0.2112
cosine_recall@5	0.3211
cosine_recall@10	0.5129
cosine_ndcg@10	0.2647
cosine_mrr@10	0.1899
cosine_map@100	0.2155

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0819
cosine_accuracy@3	0.2047
cosine_accuracy@5	0.306
cosine_accuracy@10	0.5043
cosine_precision@1	0.0819
cosine_precision@3	0.0682
cosine_precision@5	0.0612
cosine_precision@10	0.0504
cosine_recall@1	0.0819
cosine_recall@3	0.2047
cosine_recall@5	0.306
cosine_recall@10	0.5043
cosine_ndcg@10	0.2555
cosine_mrr@10	0.1808
cosine_map@100	0.2066

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0841
cosine_accuracy@3	0.2004
cosine_accuracy@5	0.3147
cosine_accuracy@10	0.4914
cosine_precision@1	0.0841
cosine_precision@3	0.0668
cosine_precision@5	0.0629
cosine_precision@10	0.0491
cosine_recall@1	0.0841
cosine_recall@3	0.2004
cosine_recall@5	0.3147
cosine_recall@10	0.4914
cosine_ndcg@10	0.2517
cosine_mrr@10	0.1795
cosine_map@100	0.2058

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0797
cosine_accuracy@3	0.2026
cosine_accuracy@5	0.3017
cosine_accuracy@10	0.4957
cosine_precision@1	0.0797
cosine_precision@3	0.0675
cosine_precision@5	0.0603
cosine_precision@10	0.0496
cosine_recall@1	0.0797
cosine_recall@3	0.2026
cosine_recall@5	0.3017
cosine_recall@10	0.4957
cosine_ndcg@10	0.2527
cosine_mrr@10	0.1796
cosine_map@100	0.2058

Training Details

Training Dataset

Unnamed Dataset

Size: 4,173 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 8 tokens
mean: 48.75 tokens
max: 125 tokens
min: 10 tokens
mean: 21.07 tokens
max: 47 tokens

Samples:

positive	anchor
`Els ajuts per a la realització d'activitats en el lleure esportiu estan destinats a les entitats sense ànim de lucre que desenvolupen activitats esportives i de lleure.`	`Quins són els sectors que es beneficien dels ajuts?`
`En el certificat s'indiquen les dades de planejament vigent, classificació del sòl, qualificació urbanística, condicions de l’edificació i usos admesos referides a una finca o solar concreta.`	`Quin és el contingut de les condicions de l'edificació en el certificat d'aprofitament urbanístic?`
`Aportació de documentació. Ajuts per compensar la disminució d'ingressos de les empreses o establiments del sector de l'hosteleria i restauració afectats per les mesures adoptades per la situació de crisis provocada pel SARS-CoV2`	`Quin és el paper dels ajuts en la situació de crisis?`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 1024, 5 768, 6 512, 7 256, 8 128, 9 64 10 ], 11 "matryoshka_weights": [ 12 1, 13 1, 14 1, 15 1, 16 1, 17 1 18 ], 19 "n_dims_per_step": -1 20}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
num_train_epochs: 10
lr_scheduler_type: cosine
warmup_ratio: 0.2
bf16: True
tf32: False
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
learning_rate: 5e-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: 10
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: False
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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_1024_cosine_map@100	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0.6130	10	3.0594	-	-	-	-	-	-
0.9808	16	-	0.2047	0.1922	0.2020	0.2016	0.1774	0.2115
1.2261	20	1.525	-	-	-	-	-	-
1.8391	30	0.7434	-	-	-	-	-	-
1.9617	32	-	0.2186	0.2003	0.2102	0.2092	0.1870	0.2101
2.4521	40	0.4451	-	-	-	-	-	-
2.9425	48	-	0.2083	0.2054	0.2091	0.2118	0.2009	0.2140
3.0651	50	0.2518	-	-	-	-	-	-
3.6782	60	0.1801	-	-	-	-	-	-
3.9847	65	-	0.2135	0.2071	0.2037	0.2115	0.2030	0.2191
4.2912	70	0.1483	-	-	-	-	-	-
4.9042	80	0.0893	-	-	-	-	-	-
4.9655	81	-	0.2066	0.2053	0.2057	0.2137	0.1982	0.2176
5.5172	90	0.0748	-	-	-	-	-	-
5.9464	97	-	0.2171	0.2113	0.2086	0.2178	0.2120	0.2193
6.1303	100	0.064	-	-	-	-	-	-
6.7433	110	0.0458	-	-	-	-	-	-
6.9885	114	-	0.2294	0.2132	0.2151	0.2227	0.2054	0.2138
7.3563	120	0.0436	-	-	-	-	-	-
7.9693	130	0.0241	0.2133	0.2083	0.2096	0.2138	0.2080	0.2124
8.5824	140	0.021	-	-	-	-	-	-
8.9502	146	-	0.216	0.2074	0.2081	0.2162	0.2094	0.2177
9.1954	150	0.0237	-	-	-	-	-	-
9.8084	160	0.0145	0.2151	0.2058	0.2066	0.2155	0.2058	0.2132

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.42.4
PyTorch: 2.4.0+cu121
Accelerate: 0.34.0.dev0
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",
}

MatryoshkaLoss

bibtex

@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

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}
}

	positive	anchor
type	string	string
details	min: 8 tokens mean: 48.75 tokens max: 125 tokens	min: 10 tokens mean: 21.07 tokens max: 47 tokens