This is a sentence-transformers model finetuned from cross-encoder/ms-marco-MiniLM-L-4-v2. It maps sentences & paragraphs to a 384-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: cross-encoder/ms-marco-MiniLM-L-4-v2
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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/sitges10242608-4ep-rerankv2")
# Run inference
sentences = [
    "Aquest tràmit permet sol·licitar la llicència per a realitzar obres d'excavació a la via pública per a la instal·lació o reparació d'infraestructures de serveis i subministraments.",
    'Quin és el paper de la via pública en aquest tràmit?',
    "Quin és l'objectiu de presentar una denúncia per presumpta infracció urbanística?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0388
cosine_accuracy@3	0.0884
cosine_accuracy@5	0.1228
cosine_accuracy@10	0.1875
cosine_precision@1	0.0388
cosine_precision@3	0.0295
cosine_precision@5	0.0246
cosine_precision@10	0.0187
cosine_recall@1	0.0388
cosine_recall@3	0.0884
cosine_recall@5	0.1228
cosine_recall@10	0.1875
cosine_ndcg@10	0.1024
cosine_mrr@10	0.0766
cosine_map@100	0.0906

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0388
cosine_accuracy@3	0.0884
cosine_accuracy@5	0.1228
cosine_accuracy@10	0.1875
cosine_precision@1	0.0388
cosine_precision@3	0.0295
cosine_precision@5	0.0246
cosine_precision@10	0.0187
cosine_recall@1	0.0388
cosine_recall@3	0.0884
cosine_recall@5	0.1228
cosine_recall@10	0.1875
cosine_ndcg@10	0.1024
cosine_mrr@10	0.0766
cosine_map@100	0.0906

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0388
cosine_accuracy@3	0.0841
cosine_accuracy@5	0.1293
cosine_accuracy@10	0.1853
cosine_precision@1	0.0388
cosine_precision@3	0.028
cosine_precision@5	0.0259
cosine_precision@10	0.0185
cosine_recall@1	0.0388
cosine_recall@3	0.0841
cosine_recall@5	0.1293
cosine_recall@10	0.1853
cosine_ndcg@10	0.1021
cosine_mrr@10	0.0767
cosine_map@100	0.0899

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0345
cosine_accuracy@3	0.0948
cosine_accuracy@5	0.1272
cosine_accuracy@10	0.1853
cosine_precision@1	0.0345
cosine_precision@3	0.0316
cosine_precision@5	0.0254
cosine_precision@10	0.0185
cosine_recall@1	0.0345
cosine_recall@3	0.0948
cosine_recall@5	0.1272
cosine_recall@10	0.1853
cosine_ndcg@10	0.101
cosine_mrr@10	0.0753
cosine_map@100	0.0899

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0345
cosine_accuracy@3	0.0841
cosine_accuracy@5	0.1034
cosine_accuracy@10	0.1703
cosine_precision@1	0.0345
cosine_precision@3	0.028
cosine_precision@5	0.0207
cosine_precision@10	0.017
cosine_recall@1	0.0345
cosine_recall@3	0.0841
cosine_recall@5	0.1034
cosine_recall@10	0.1703
cosine_ndcg@10	0.0933
cosine_mrr@10	0.07
cosine_map@100	0.0837

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: 10 tokens
mean: 67.49 tokens
max: 214 tokens
min: 11 tokens
mean: 28.0 tokens
max: 61 tokens

Samples:

positive	anchor
`Havent-se d'acreditar la matriculació i inscripció en el respectiu centre públic o concertat, així com el cost de les llars d'infants, de l'educació especialitzada per les discapacitats físiques, psíquiques i sensorials en centres públics, concertats o privats.`	`Quin és el requisit per acreditar la llar d'infants?`
`El volant històric de convivència és el document que informa de la residencia en el municipi de Sitges, així com altres fets relatius a l'empadronament d'una persona, i detalla tots els domicilis, la data inicial i final en els que ha estat empadronada en cadascun d'ells, i les persones amb les què constava inscrites, segons les dades que consten al Padró Municipal d'Habitants fins a la data d'expedició.`	`Quin és el propòsit del volant històric de convivència?`
`Instal·lació de tanques sense obra.`	`Quins són els exemples d'instal·lacions que es poden comunicar amb aquest tràmit?`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 384, 5 256, 6 128, 7 64 8 ], 9 "matryoshka_weights": [ 10 1, 11 1, 12 1, 13 1 14 ], 15 "n_dims_per_step": -1 16}

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: 5
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: 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: 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_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	11.3695	-	-	-	-	-
0.9808	16	-	0.0214	0.0243	0.0234	0.0199	0.0234
1.2261	20	10.653	-	-	-	-	-
1.8391	30	9.0745	-	-	-	-	-
1.9617	32	-	0.0495	0.0517	0.0589	0.0481	0.0589
2.4521	40	7.3468	-	-	-	-	-
2.9425	48	-	0.0764	0.0734	0.0811	0.0709	0.0811
3.0651	50	5.887	-	-	-	-	-
3.6782	60	5.3568	-	-	-	-	-
3.9847	65	-	0.0922	0.0857	0.0896	0.0808	0.0896
4.2912	70	4.8338	-	-	-	-	-
4.9042	80	4.9251	0.0899	0.0899	0.0906	0.0837	0.0906

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: 10 tokens mean: 67.49 tokens max: 214 tokens	min: 11 tokens mean: 28.0 tokens max: 61 tokens