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

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("adriansanz/ST-tramits-sitges-003-10ep")
# Run inference
sentences = [
    "Els comerços locals obtenen un benefici principal de la implementació del projecte d'implantació i ús de la targeta de fidelització del comerç local de Sitges, que és la possibilitat d'augmentar les vendes i la fidelització dels clients.",
    "Quin és el benefici que els comerços locals obtenen de la implementació del projecte d'implantació i ús de la targeta de fidelització?",
    'Quin és el propòsit de la deixalleria municipal per a l’ambient?',
]
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	0.1331
cosine_accuracy@3	0.2624
cosine_accuracy@5	0.3536
cosine_accuracy@10	0.5243
cosine_precision@1	0.1331
cosine_precision@3	0.0875
cosine_precision@5	0.0707
cosine_precision@10	0.0524
cosine_recall@1	0.1331
cosine_recall@3	0.2624
cosine_recall@5	0.3536
cosine_recall@10	0.5243
cosine_ndcg@10	0.2986
cosine_mrr@10	0.2301
cosine_map@100	0.2513

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1322
cosine_accuracy@3	0.263
cosine_accuracy@5	0.3541
cosine_accuracy@10	0.5286
cosine_precision@1	0.1322
cosine_precision@3	0.0877
cosine_precision@5	0.0708
cosine_precision@10	0.0529
cosine_recall@1	0.1322
cosine_recall@3	0.263
cosine_recall@5	0.3541
cosine_recall@10	0.5286
cosine_ndcg@10	0.3011
cosine_mrr@10	0.2322
cosine_map@100	0.253

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1342
cosine_accuracy@3	0.2655
cosine_accuracy@5	0.3589
cosine_accuracy@10	0.5257
cosine_precision@1	0.1342
cosine_precision@3	0.0885
cosine_precision@5	0.0718
cosine_precision@10	0.0526
cosine_recall@1	0.1342
cosine_recall@3	0.2655
cosine_recall@5	0.3589
cosine_recall@10	0.5257
cosine_ndcg@10	0.3011
cosine_mrr@10	0.2329
cosine_map@100	0.2538

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1266
cosine_accuracy@3	0.2633
cosine_accuracy@5	0.3564
cosine_accuracy@10	0.5229
cosine_precision@1	0.1266
cosine_precision@3	0.0878
cosine_precision@5	0.0713
cosine_precision@10	0.0523
cosine_recall@1	0.1266
cosine_recall@3	0.2633
cosine_recall@5	0.3564
cosine_recall@10	0.5229
cosine_ndcg@10	0.2972
cosine_mrr@10	0.2285
cosine_map@100	0.2496

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1274
cosine_accuracy@3	0.2684
cosine_accuracy@5	0.3553
cosine_accuracy@10	0.521
cosine_precision@1	0.1274
cosine_precision@3	0.0895
cosine_precision@5	0.0711
cosine_precision@10	0.0521
cosine_recall@1	0.1274
cosine_recall@3	0.2684
cosine_recall@5	0.3553
cosine_recall@10	0.521
cosine_ndcg@10	0.2973
cosine_mrr@10	0.2293
cosine_map@100	0.2507

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1224
cosine_accuracy@3	0.2546
cosine_accuracy@5	0.344
cosine_accuracy@10	0.5165
cosine_precision@1	0.1224
cosine_precision@3	0.0849
cosine_precision@5	0.0688
cosine_precision@10	0.0516
cosine_recall@1	0.1224
cosine_recall@3	0.2546
cosine_recall@5	0.344
cosine_recall@10	0.5165
cosine_ndcg@10	0.2909
cosine_mrr@10	0.2225
cosine_map@100	0.2429

Training Details

Training Dataset

json

Dataset: json
Size: 6,399 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 9 tokens
mean: 49.44 tokens
max: 178 tokens

min: 9 tokens
mean: 21.17 tokens
max: 48 tokens

	positive	anchor
type	string	string
details	min: 9 tokens mean: 49.44 tokens max: 178 tokens	min: 9 tokens mean: 21.17 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:

{
    "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: 10
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: 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: 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
0.4	10	3.5464	-	-	-	-	-	-
0.8	20	2.3861	-	-	-	-	-	-
1.0	25	-	0.2327	0.2144	0.2252	0.2286	0.1938	0.2329
1.1975	30	1.8712	-	-	-	-	-	-
1.5975	40	1.3322	-	-	-	-	-	-
1.9975	50	0.9412	0.2410	0.2310	0.2383	0.2415	0.2236	0.2436
2.395	60	0.806	-	-	-	-	-	-
2.795	70	0.5024	-	-	-	-	-	-
2.995	75	-	0.2451	0.2384	0.2455	0.2487	0.2323	0.2423
3.1925	80	0.4259	-	-	-	-	-	-
3.5925	90	0.3556	-	-	-	-	-	-
3.9925	100	0.2555	0.2477	0.2443	0.2417	0.2485	0.2369	0.2470
4.39	110	0.2611	-	-	-	-	-	-
4.79	120	0.1939	-	-	-	-	-	-
4.99	125	-	0.2490	0.2425	0.2479	0.2485	0.2386	0.2495
5.1875	130	0.2021	-	-	-	-	-	-
5.5875	140	0.1537	-	-	-	-	-	-
5.9875	150	0.1277	0.2535	0.2491	0.2491	0.2534	0.2403	0.2541
6.385	160	0.1213	-	-	-	-	-	-
6.785	170	0.1035	-	-	-	-	-	-
6.985	175	-	0.2513	0.2493	0.2435	0.2515	0.2380	0.2528
7.1825	180	0.0965	-	-	-	-	-	-
7.5825	190	0.0861	-	-	-	-	-	-
7.9825	200	0.0794	0.2529	0.2536	0.2526	0.2545	0.2438	0.2570
8.38	210	0.0734	-	-	-	-	-	-
8.78	220	0.066	-	-	-	-	-	-
8.98	225	-	0.2538	0.2523	0.2519	0.2542	0.2457	0.2572
9.1775	230	0.0731	-	-	-	-	-	-
9.5775	240	0.0726	-	-	-	-	-	-
9.9775	250	0.0632	0.2513	0.2507	0.2496	0.2538	0.2429	0.2530

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

@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}
}

adriansanz
/

ST-tramits-sitges-003-10ep

SentenceTransformer based on BAAI/bge-m3

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Training Details

Training Dataset

json

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

Model tree for adriansanz/ST-tramits-sitges-003-10ep

Evaluation results