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

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/ST-tramits-sitges-003-5ep")
# Run inference
sentences = [
    'A la nostra vila hi ha veïns i veïnes que els agradaria tornar a fer de pagès o provar-ho per primera vegada.',
    "Quin és l'objectiu principal de l'activitat del Viver dels Avis de Sitges?",
    'Quin és el paper de les persones en relació amb les indemnitzacions?',
]
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	0.1105
cosine_accuracy@3	0.227
cosine_accuracy@5	0.3055
cosine_accuracy@10	0.4532
cosine_precision@1	0.1105
cosine_precision@3	0.0757
cosine_precision@5	0.0611
cosine_precision@10	0.0453
cosine_recall@1	0.1105
cosine_recall@3	0.227
cosine_recall@5	0.3055
cosine_recall@10	0.4532
cosine_ndcg@10	0.2562
cosine_mrr@10	0.1965
cosine_map@100	0.2186

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1156
cosine_accuracy@3	0.2321
cosine_accuracy@5	0.3114
cosine_accuracy@10	0.4456
cosine_precision@1	0.1156
cosine_precision@3	0.0774
cosine_precision@5	0.0623
cosine_precision@10	0.0446
cosine_recall@1	0.1156
cosine_recall@3	0.2321
cosine_recall@5	0.3114
cosine_recall@10	0.4456
cosine_ndcg@10	0.258
cosine_mrr@10	0.2009
cosine_map@100	0.2234

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1038
cosine_accuracy@3	0.2211
cosine_accuracy@5	0.297
cosine_accuracy@10	0.4397
cosine_precision@1	0.1038
cosine_precision@3	0.0737
cosine_precision@5	0.0594
cosine_precision@10	0.044
cosine_recall@1	0.1038
cosine_recall@3	0.2211
cosine_recall@5	0.297
cosine_recall@10	0.4397
cosine_ndcg@10	0.2474
cosine_mrr@10	0.1889
cosine_map@100	0.2118

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1004
cosine_accuracy@3	0.2152
cosine_accuracy@5	0.2979
cosine_accuracy@10	0.4439
cosine_precision@1	0.1004
cosine_precision@3	0.0717
cosine_precision@5	0.0596
cosine_precision@10	0.0444
cosine_recall@1	0.1004
cosine_recall@3	0.2152
cosine_recall@5	0.2979
cosine_recall@10	0.4439
cosine_ndcg@10	0.248
cosine_mrr@10	0.1883
cosine_map@100	0.2113

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1089
cosine_accuracy@3	0.2262
cosine_accuracy@5	0.303
cosine_accuracy@10	0.4414
cosine_precision@1	0.1089
cosine_precision@3	0.0754
cosine_precision@5	0.0606
cosine_precision@10	0.0441
cosine_recall@1	0.1089
cosine_recall@3	0.2262
cosine_recall@5	0.303
cosine_recall@10	0.4414
cosine_ndcg@10	0.2537
cosine_mrr@10	0.1964
cosine_map@100	0.2188

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0937
cosine_accuracy@3	0.2
cosine_accuracy@5	0.2743
cosine_accuracy@10	0.4177
cosine_precision@1	0.0937
cosine_precision@3	0.0667
cosine_precision@5	0.0549
cosine_precision@10	0.0418
cosine_recall@1	0.0937
cosine_recall@3	0.2
cosine_recall@5	0.2743
cosine_recall@10	0.4177
cosine_ndcg@10	0.2305
cosine_mrr@10	0.1738
cosine_map@100	0.1978

Training Details

Training Dataset

json

Dataset: json
Size: 8,769 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 5 tokens
mean: 49.22 tokens
max: 178 tokens
min: 10 tokens
mean: 20.94 tokens
max: 48 tokens

Samples:

positive	anchor
`L'Ajuntament de Sitges atorga subvencions per a projectes i activitats d'interès públic o social que tinguin per finalitat les activitats esportives federades, escolars o populars desenvolupades per les entitats esportives i esportistes del municipi de Sitges.`	`Quin és el benefici de les subvencions per a les entitats esportives?`
`L'Ajuntament de Sitges atorga subvencions per a projectes i activitats d'interès públic o social que tinguin per finalitat les activitats esportives federades, escolars o populars desenvolupades per les entitats esportives i esportistes del municipi de Sitges al llarg de l'exercici per la qual es sol·licita la subvenció, i reuneixin les condicions assenyalades a les bases.`	`Quin és el període d'execució dels projectes i activitats esportives?`
`Certificat on s'indica el nombre d'habitatges que configuren el padró de l'Impost sobre Béns Immobles del municipi o bé d'una part d'aquest.`	`Quin és el contingut del certificat del nombre d'habitatges?`

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
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_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0.2914	10	3.6318	-	-	-	-	-	-
0.5829	20	2.329	-	-	-	-	-	-
0.8743	30	1.5614	-	-	-	-	-	-
0.9909	34	-	0.2055	0.1998	0.2020	0.2001	0.1903	0.2019
1.1658	40	1.2383	-	-	-	-	-	-
1.4572	50	0.9323	-	-	-	-	-	-
1.7486	60	0.6616	-	-	-	-	-	-
1.9818	68	-	0.2244	0.2063	0.2223	0.2166	0.2011	0.2235
2.0401	70	0.5545	-	-	-	-	-	-
2.3315	80	0.5043	-	-	-	-	-	-
2.6230	90	0.3542	-	-	-	-	-	-
2.9144	100	0.3095	-	-	-	-	-	-
2.9727	102	-	0.2224	0.2046	0.2170	0.2100	0.1986	0.2144
3.2058	110	0.2863	-	-	-	-	-	-
3.4973	120	0.2329	-	-	-	-	-	-
3.7887	130	0.2353	-	-	-	-	-	-
3.9927	137	-	0.2197	0.2112	0.2098	0.2154	0.1949	0.2178
4.0801	140	0.1759	-	-	-	-	-	-
4.3716	150	0.2308	-	-	-	-	-	-
4.6630	160	0.1656	-	-	-	-	-	-
4.9545	170	0.1812	0.2186	0.2188	0.2113	0.2118	0.1978	0.2234

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.1.1
Transformers: 4.44.2
PyTorch: 2.4.1+cu121
Accelerate: 0.35.0.dev0
Datasets: 3.0.1
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: 5 tokens mean: 49.22 tokens max: 178 tokens	min: 10 tokens mean: 20.94 tokens max: 48 tokens