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-SQV-006-5ep")
# Run inference
sentences = [
    'L’Ajuntament de Sant Quirze del Vallès reconeix un dret preferent al titular del dret funerari sobre la corresponent sepultura o al successor o causahavent de l’anterior titular d’aquest dret, que permet adquirir de nou el dret funerari referit, sobre la mateixa sepultura, un cop el dret atorgat ha exhaurit el termini de vigència',
    'Quan es pot adquirir de nou el dret funerari?',
    'Quin és el paper del cens electoral en les eleccions?',
]
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	0.1017
cosine_accuracy@3	0.2771
cosine_accuracy@5	0.368
cosine_accuracy@10	0.4827
cosine_precision@1	0.1017
cosine_precision@3	0.0924
cosine_precision@5	0.0736
cosine_precision@10	0.0483
cosine_recall@1	0.1017
cosine_recall@3	0.2771
cosine_recall@5	0.368
cosine_recall@10	0.4827
cosine_ndcg@10	0.2757
cosine_mrr@10	0.2113
cosine_map@100	0.2287

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.119
cosine_accuracy@3	0.29
cosine_accuracy@5	0.3658
cosine_accuracy@10	0.4957
cosine_precision@1	0.119
cosine_precision@3	0.0967
cosine_precision@5	0.0732
cosine_precision@10	0.0496
cosine_recall@1	0.119
cosine_recall@3	0.29
cosine_recall@5	0.3658
cosine_recall@10	0.4957
cosine_ndcg@10	0.2892
cosine_mrr@10	0.2253
cosine_map@100	0.2428

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1082
cosine_accuracy@3	0.2662
cosine_accuracy@5	0.3636
cosine_accuracy@10	0.5065
cosine_precision@1	0.1082
cosine_precision@3	0.0887
cosine_precision@5	0.0727
cosine_precision@10	0.0506
cosine_recall@1	0.1082
cosine_recall@3	0.2662
cosine_recall@5	0.3636
cosine_recall@10	0.5065
cosine_ndcg@10	0.2839
cosine_mrr@10	0.2156
cosine_map@100	0.2323

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1147
cosine_accuracy@3	0.2403
cosine_accuracy@5	0.3398
cosine_accuracy@10	0.4805
cosine_precision@1	0.1147
cosine_precision@3	0.0801
cosine_precision@5	0.068
cosine_precision@10	0.0481
cosine_recall@1	0.1147
cosine_recall@3	0.2403
cosine_recall@5	0.3398
cosine_recall@10	0.4805
cosine_ndcg@10	0.275
cosine_mrr@10	0.212
cosine_map@100	0.2304

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1126
cosine_accuracy@3	0.2641
cosine_accuracy@5	0.329
cosine_accuracy@10	0.487
cosine_precision@1	0.1126
cosine_precision@3	0.088
cosine_precision@5	0.0658
cosine_precision@10	0.0487
cosine_recall@1	0.1126
cosine_recall@3	0.2641
cosine_recall@5	0.329
cosine_recall@10	0.487
cosine_ndcg@10	0.2791
cosine_mrr@10	0.2152
cosine_map@100	0.234

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1039
cosine_accuracy@3	0.2619
cosine_accuracy@5	0.3355
cosine_accuracy@10	0.474
cosine_precision@1	0.1039
cosine_precision@3	0.0873
cosine_precision@5	0.0671
cosine_precision@10	0.0474
cosine_recall@1	0.1039
cosine_recall@3	0.2619
cosine_recall@5	0.3355
cosine_recall@10	0.474
cosine_ndcg@10	0.27
cosine_mrr@10	0.2071
cosine_map@100	0.2256

Training Details

Training Dataset

json

Dataset: json
Size: 6,468 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 5 tokens
mean: 39.4 tokens
max: 168 tokens
min: 10 tokens
mean: 20.48 tokens
max: 44 tokens

Samples:

positive	anchor
`Aquest tràmit permet la inscripció al padró dels canvis de domicili dins de Sant Quirze del Vallès...`	`Quin és el benefici de la inscripció al Padró d'Habitants?`
`Els recursos que es poden oferir al banc de recursos són: MATERIALS, PROFESSIONALS i SOCIALS.`	`Quins tipus de recursos es poden oferir al banc de recursos?`
`El termini per a la presentació de sol·licituds serà del 8 al 21 de maig de 2024, ambdós inclosos.`	`Quin és el termini per a la presentació de sol·licituds per a la preinscripció a l'Escola Bressol Municipal El Patufet?`

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
0.3951	10	4.4042	-	-	-	-	-	-
0.7901	20	2.9471	-	-	-	-	-	-
0.9877	25	-	0.2293	0.2045	0.2099	0.2138	0.1717	0.2242
1.1852	30	2.2351	-	-	-	-	-	-
1.5802	40	1.5289	-	-	-	-	-	-
1.9753	50	1.2045	0.2332	0.2182	0.2277	0.2221	0.2051	0.2248
2.3704	60	0.9435	-	-	-	-	-	-
2.7654	70	0.7958	-	-	-	-	-	-
2.963	75	-	0.2379	0.2352	0.2276	0.2204	0.2138	0.2235
3.1605	80	0.6703	-	-	-	-	-	-
3.5556	90	0.6162	-	-	-	-	-	-
3.9506	100	0.6079	-	-	-	-	-	-
3.9901	101	-	0.2251	0.2307	0.2201	0.2343	0.2210	0.2348
4.3457	110	0.5085	-	-	-	-	-	-
4.7407	120	0.5248	-	-	-	-	-	-
4.9383	125	-	0.2287	0.2340	0.2304	0.2323	0.2256	0.2428

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: 39.4 tokens max: 168 tokens	min: 10 tokens mean: 20.48 tokens max: 44 tokens