SentenceTransformer based on cross-encoder/ms-marco-MiniLM-L-4-v2

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("adriansanz/sitges10242608-4ep-rerankv3")
# 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	0.0517
cosine_accuracy@3	0.1272
cosine_accuracy@5	0.1789
cosine_accuracy@10	0.3254
cosine_precision@1	0.0517
cosine_precision@3	0.0424
cosine_precision@5	0.0358
cosine_precision@10	0.0325
cosine_recall@1	0.0517
cosine_recall@3	0.1272
cosine_recall@5	0.1789
cosine_recall@10	0.3254
cosine_ndcg@10	0.1628
cosine_mrr@10	0.1143
cosine_map@100	0.1362

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0517
cosine_accuracy@3	0.1272
cosine_accuracy@5	0.1789
cosine_accuracy@10	0.3254
cosine_precision@1	0.0517
cosine_precision@3	0.0424
cosine_precision@5	0.0358
cosine_precision@10	0.0325
cosine_recall@1	0.0517
cosine_recall@3	0.1272
cosine_recall@5	0.1789
cosine_recall@10	0.3254
cosine_ndcg@10	0.1628
cosine_mrr@10	0.1143
cosine_map@100	0.1362

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0453
cosine_accuracy@3	0.1207
cosine_accuracy@5	0.1703
cosine_accuracy@10	0.3233
cosine_precision@1	0.0453
cosine_precision@3	0.0402
cosine_precision@5	0.0341
cosine_precision@10	0.0323
cosine_recall@1	0.0453
cosine_recall@3	0.1207
cosine_recall@5	0.1703
cosine_recall@10	0.3233
cosine_ndcg@10	0.1576
cosine_mrr@10	0.1083
cosine_map@100	0.1311

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0474
cosine_accuracy@3	0.1207
cosine_accuracy@5	0.1767
cosine_accuracy@10	0.3147
cosine_precision@1	0.0474
cosine_precision@3	0.0402
cosine_precision@5	0.0353
cosine_precision@10	0.0315
cosine_recall@1	0.0474
cosine_recall@3	0.1207
cosine_recall@5	0.1767
cosine_recall@10	0.3147
cosine_ndcg@10	0.1556
cosine_mrr@10	0.1083
cosine_map@100	0.1316

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0366
cosine_accuracy@3	0.1013
cosine_accuracy@5	0.153
cosine_accuracy@10	0.2845
cosine_precision@1	0.0366
cosine_precision@3	0.0338
cosine_precision@5	0.0306
cosine_precision@10	0.0284
cosine_recall@1	0.0366
cosine_recall@3	0.1013
cosine_recall@5	0.153
cosine_recall@10	0.2845
cosine_ndcg@10	0.1358
cosine_mrr@10	0.0917
cosine_map@100	0.1149

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

	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:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        384,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
num_train_epochs: 10
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: 32
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: 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: 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
0.9771	8	-	0.0953	0.0965	0.0957	0.0841	0.0957
1.2214	10	6.7779	-	-	-	-	-
1.9542	16	-	0.1056	0.1036	0.1078	0.0948	0.1078
2.4427	20	5.8485	-	-	-	-	-
2.9313	24	-	0.1112	0.1107	0.1170	0.1009	0.1170
3.6641	30	4.6394	-	-	-	-	-
3.9084	32	-	0.1243	0.1189	0.1247	0.1152	0.1247
4.8855	40	3.8786	0.1248	0.1248	0.1335	0.1148	0.1335
5.9847	49	-	0.1298	0.1298	0.1371	0.1204	0.1371
6.1069	50	3.3198	-	-	-	-	-
6.9618	57	-	0.1284	0.1347	0.1370	0.1208	0.1370
7.3282	60	3.081	-	-	-	-	-
7.9389	65	-	0.1273	0.1344	0.1360	0.1215	0.1360
8.5496	70	2.8556	-	-	-	-	-
8.9160	73	-	0.1313	0.1315	0.1350	0.1147	0.1350
9.771	80	2.7635	0.1316	0.1311	0.1362	0.1149	0.1362

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

@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
/

sitges10242608-4ep-rerankv3

SentenceTransformer based on cross-encoder/ms-marco-MiniLM-L-4-v2

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

Training Details

Training Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

Model tree for adriansanz/sitges10242608-4ep-rerankv3

Evaluation results