metadata

language:
  - en
  - ca
license: apache-2.0
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:1K<n<10K
  - loss:CoSENTLoss
base_model: microsoft/mpnet-base
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
widget:
  - source_sentence: Dia Internacional del Nen Prematur
    sentences:
      - Premiats a les comarques de Barcelona
      - Les concordances són adjectiu / substantiu o verb / substantiu.
      - >-
        Els Mossos en busquen un altre, que va aconseguir fugir en ser enxampats
        'in fraganti'
  - source_sentence: Vulneració del dret a la llibertat
    sentences:
      - Vulneració del dret a un jutge imparcial
      - Detenen un home a Malgrat de Mar per apallissar un escombriaire
      - La víctima ha rebut un cop de puny i ha caigut a terra inconscient
  - source_sentence: Agafem un taxi i ens plantem allà.
    sentences:
      - És una activitat gratuïta oberta al públic general.
      - El líder del PSC, Miquel Iceta, serà el nou president del Senat
      - >-
        El PSOE ja no descarta l’aplicació de l’article 155 de la Constitució a
        Catalunya
  - source_sentence: No ho entenc, però és el que hi ha.
    sentences:
      - és dels plats que a casa ens encanten!
      - El Punt d'Informació Juvenil és el servei més actiu del centre.
      - Puigdemont reunirà dimecres a Bèlgica els diputats de JxCat
  - source_sentence: Però que hi ha de cert en tot això?
    sentences:
      - Però, què hi ha de veritat en tot això?
      - Els camioners dissolen la marxa lenta a les rondes de Barcelona
      - >-
        El 112 atén 747.730 trucades durant el primer semestre, un 9,6% més que
        l'any passat
pipeline_tag: sentence-similarity
model-index:
  - name: MPNet base trained on semantic text similarity
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: pearson_cosine
            value: 0.9369799393019737
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.991833254558149
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.9582116235734125
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.9876060961452328
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.9594231143506534
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.9887559900790531
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.9469313911363318
            name: Pearson Dot
          - type: spearman_dot
            value: 0.9834282009396937
            name: Spearman Dot
          - type: pearson_max
            value: 0.9594231143506534
            name: Pearson Max
          - type: spearman_max
            value: 0.991833254558149
            name: Spearman Max
          - type: pearson_cosine
            value: 0.5855972037779524
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.5854785473306573
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.5881281979560977
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.578667646485271
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.5851079475768374
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.5754637407144132
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.5612927132777441
            name: Pearson Dot
          - type: spearman_dot
            value: 0.5630862098985
            name: Spearman Dot
          - type: pearson_max
            value: 0.5881281979560977
            name: Pearson Max
          - type: spearman_max
            value: 0.5854785473306573
            name: Spearman Max
          - type: pearson_cosine
            value: 0.6501162382185041
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6819594226888658
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.6517756634326819
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.6701084565797553
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.6553647425414415
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.675292747578234
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.6350099608995957
            name: Pearson Dot
          - type: spearman_dot
            value: 0.6458150666120989
            name: Spearman Dot
          - type: pearson_max
            value: 0.6553647425414415
            name: Pearson Max
          - type: spearman_max
            value: 0.6819594226888658
            name: Spearman Max

MPNet base trained on semantic text similarity

This is a sentence-transformers model finetuned from microsoft/mpnet-base on the projecte-aina/sts-ca dataset. It maps sentences & paragraphs to a 768-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: microsoft/mpnet-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- projecte-aina/sts-ca
Languages: en, ca
License: apache-2.0

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: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, '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("pauhidalgoo/finetuned-sts-ca-mpnet-base")
# Run inference
sentences = [
    'Però que hi ha de cert en tot això?',
    'Però, què hi ha de veritat en tot això?',
    'Els camioners dissolen la marxa lenta a les rondes de Barcelona',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.937
spearman_cosine	0.9918
pearson_manhattan	0.9582
spearman_manhattan	0.9876
pearson_euclidean	0.9594
spearman_euclidean	0.9888
pearson_dot	0.9469
spearman_dot	0.9834
pearson_max	0.9594
spearman_max	0.9918

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.5856
spearman_cosine	0.5855
pearson_manhattan	0.5881
spearman_manhattan	0.5787
pearson_euclidean	0.5851
spearman_euclidean	0.5755
pearson_dot	0.5613
spearman_dot	0.5631
pearson_max	0.5881
spearman_max	0.5855

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6501
spearman_cosine	0.682
pearson_manhattan	0.6518
spearman_manhattan	0.6701
pearson_euclidean	0.6554
spearman_euclidean	0.6753
pearson_dot	0.635
spearman_dot	0.6458
pearson_max	0.6554
spearman_max	0.682

Training Details

Training Dataset

projecte-aina/sts-ca

Dataset: projecte-aina/sts-ca
Size: 2,073 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string float
details
min: 10 tokens
mean: 32.36 tokens
max: 82 tokens

min: 11 tokens
mean: 30.57 tokens
max: 68 tokens

min: 0.0
mean: 2.56
max: 5.0

	sentence1	sentence2	label
type	string	string	float
details	min: 10 tokens mean: 32.36 tokens max: 82 tokens	min: 11 tokens mean: 30.57 tokens max: 68 tokens	min: 0.0 mean: 2.56 max: 5.0

Samples:

sentence1	sentence2	label
`Atorga per primer cop les mencions Encarna Sanahuja a la inclusió de la perspectiva de gènere en docència Universitària`	`Creen la menció M. Encarna Sanahuja a la inclusió de la perspectiva de gènere en docència universitària`	`3.5`
`Finalment, afegiu-hi els bolets que haureu saltat en una paella amb oli i deixeu-ho coure tot junt durant 5 minuts.`	`Finalment, poseu-hi les minipastanagues tallades a dauets, els pèsols, rectifiqueu-ho de sal i deixeu-ho coure tot junt durant un parell de minuts més.`	`1.25`
`El TC suspèn el pla d'acció exterior i de relacions amb la UE de la Generalitat`	`El Constitucional manté la suspensió del pla estratègic d'acció exterior i relacions amb la UE`	`3.6700000762939453`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Evaluation Dataset

projecte-aina/sts-ca

Dataset: projecte-aina/sts-ca
Size: 500 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string float
details
min: 10 tokens
mean: 32.94 tokens
max: 68 tokens

min: 12 tokens
mean: 31.42 tokens
max: 69 tokens

min: 0.0
mean: 2.58
max: 5.0

	sentence1	sentence2	label
type	string	string	float
details	min: 10 tokens mean: 32.94 tokens max: 68 tokens	min: 12 tokens mean: 31.42 tokens max: 69 tokens	min: 0.0 mean: 2.58 max: 5.0

Samples:

sentence1	sentence2	label
`L'euríbor puja una centèsima fins el -0,189% al gener després de setze mesos de caigudes`	`La morositat de bancs i caixes repunta moderadament fins el 9,44%, després d'onze mesos de caigudes`	`1.6699999570846558`
`Demanen 3 anys de presó a l'ex treballador d'una farmàcia de Lleida per robar més de 550 unitats de Viagra i Cialis`	`L'extreballador d'una farmàcia de Lleida accepta sis mesos de presó per robar més de 500 unitats de Viagra i Cialis`	`2.0`
`Es tracta d'un jove de 20 anys que ha estat denunciat als Mossos d'Esquadra`	`Es tracta d'un jove de 21 anys que ha estat denunciat penalment pels Mossos`	`3.0`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 40
warmup_ratio: 0.1
fp16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
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: 1
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: 40
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: False
fp16: True
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
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: False
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
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	spearman_cosine
3.8462	500	4.5209	-
7.6923	1000	4.1445	-
11.5385	1500	3.9291	-
15.3846	2000	3.6952	-
19.2308	2500	3.5393	-
23.0769	3000	3.3778	-
26.9231	3500	3.1712	-
30.7692	4000	2.8265	-
34.6154	4500	2.6265	-
38.4615	5000	2.3259	-
40.0	5200	-	0.6820

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.0
Transformers: 4.41.1
PyTorch: 2.3.0+cu121
Accelerate: 0.30.1
Datasets: 2.19.2
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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}