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/sqv2")
# Run inference
sentences = [
    'La presentació de la sol·licitud no dona dret al muntatge de la parada.',
    'Quin és el requisit per a la presentació de la sol·licitud d’autorització?',
    'Quin és el motiu per canviar la persona titular dels drets funeraris?',
]
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	0.044
cosine_accuracy@3	0.116
cosine_accuracy@5	0.18
cosine_accuracy@10	0.3507
cosine_precision@1	0.044
cosine_precision@3	0.0387
cosine_precision@5	0.036
cosine_precision@10	0.0351
cosine_recall@1	0.044
cosine_recall@3	0.116
cosine_recall@5	0.18
cosine_recall@10	0.3507
cosine_ndcg@10	0.1659
cosine_mrr@10	0.111
cosine_map@100	0.1341

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0413
cosine_accuracy@3	0.116
cosine_accuracy@5	0.1787
cosine_accuracy@10	0.3627
cosine_precision@1	0.0413
cosine_precision@3	0.0387
cosine_precision@5	0.0357
cosine_precision@10	0.0363
cosine_recall@1	0.0413
cosine_recall@3	0.116
cosine_recall@5	0.1787
cosine_recall@10	0.3627
cosine_ndcg@10	0.169
cosine_mrr@10	0.1116
cosine_map@100	0.1341

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0467
cosine_accuracy@3	0.116
cosine_accuracy@5	0.1787
cosine_accuracy@10	0.356
cosine_precision@1	0.0467
cosine_precision@3	0.0387
cosine_precision@5	0.0357
cosine_precision@10	0.0356
cosine_recall@1	0.0467
cosine_recall@3	0.116
cosine_recall@5	0.1787
cosine_recall@10	0.356
cosine_ndcg@10	0.1677
cosine_mrr@10	0.1121
cosine_map@100	0.1346

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0387
cosine_accuracy@3	0.1067
cosine_accuracy@5	0.1707
cosine_accuracy@10	0.3413
cosine_precision@1	0.0387
cosine_precision@3	0.0356
cosine_precision@5	0.0341
cosine_precision@10	0.0341
cosine_recall@1	0.0387
cosine_recall@3	0.1067
cosine_recall@5	0.1707
cosine_recall@10	0.3413
cosine_ndcg@10	0.1587
cosine_mrr@10	0.1046
cosine_map@100	0.129

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0493
cosine_accuracy@3	0.1227
cosine_accuracy@5	0.1987
cosine_accuracy@10	0.3667
cosine_precision@1	0.0493
cosine_precision@3	0.0409
cosine_precision@5	0.0397
cosine_precision@10	0.0367
cosine_recall@1	0.0493
cosine_recall@3	0.1227
cosine_recall@5	0.1987
cosine_recall@10	0.3667
cosine_ndcg@10	0.1759
cosine_mrr@10	0.119
cosine_map@100	0.142

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0373
cosine_accuracy@3	0.0947
cosine_accuracy@5	0.1573
cosine_accuracy@10	0.34
cosine_precision@1	0.0373
cosine_precision@3	0.0316
cosine_precision@5	0.0315
cosine_precision@10	0.034
cosine_recall@1	0.0373
cosine_recall@3	0.0947
cosine_recall@5	0.1573
cosine_recall@10	0.34
cosine_ndcg@10	0.1535
cosine_mrr@10	0.0987
cosine_map@100	0.1226

Training Details

Training Dataset

json

Dataset: json
Size: 6,749 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 6 tokens
mean: 42.03 tokens
max: 106 tokens

min: 10 tokens
mean: 20.32 tokens
max: 54 tokens

	positive	anchor
type	string	string
details	min: 6 tokens mean: 42.03 tokens max: 106 tokens	min: 10 tokens mean: 20.32 tokens max: 54 tokens

Samples:

positive	anchor
`Aquest tràmit us permet compensar deutes de naturalesa pública a favor de l'Ajuntament, sigui quin sigui el seu estat (voluntari/executiu), amb crèdits reconeguts per aquest a favor del mateix deutor, i que el seu estat sigui pendent de pagament.`	`Quin és el benefici de la compensació de deutes amb crèdits?`
`El seu objecte és que -prèviament a la seva execució material- l'Ajuntament comprovi l'adequació de l’actuació a la normativa i planejament, així com a les ordenances municipals sobre l’ús del sòl i edificació.`	`Quin és el paper de les ordenances municipals en aquest tràmit?`
`Comunicació prèvia del manteniment en espais, zones o instal·lacions comunitàries interiors dels edificis (reparació i/o millora de materials).`	`Quin és el límit del manteniment en espais comunitaris interiors dels edificis?`

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_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0.3791	10	3.0867	-	-	-	-	-	-
0.7583	20	2.4414	-	-	-	-	-	-
0.9858	26	-	0.1266	0.1255	0.1232	0.1257	0.1091	0.1345
1.1351	30	1.7091	-	-	-	-	-	-
1.5142	40	1.2495	-	-	-	-	-	-
1.8934	50	0.9813	-	-	-	-	-	-
1.9692	52	-	0.1315	0.1325	0.1285	0.1328	0.1218	0.1309
2.2701	60	0.6918	-	-	-	-	-	-
2.6493	70	0.7146	-	-	-	-	-	-
2.9905	79	-	0.1370	0.1344	0.1355	0.1338	0.1269	0.1363
3.0261	80	0.6002	-	-	-	-	-	-
3.4052	90	0.4816	-	-	-	-	-	-
3.7844	100	0.4949	-	-	-	-	-	-
3.9739	105	-	0.1357	0.1393	0.1302	0.1347	0.1204	0.1354
4.1611	110	0.474	-	-	-	-	-	-
4.5403	120	0.4692	-	-	-	-	-	-
4.9194	130	0.4484	0.1341	0.142	0.129	0.1346	0.1226	0.1341

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.1.0
Transformers: 4.44.2
PyTorch: 2.4.0+cu121
Accelerate: 0.35.0.dev0
Datasets: 3.0.0
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
/

sqv-v2

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/sqv-v2

Evaluation results