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.

python

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

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:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

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

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:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

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

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",
}

CoSENTLoss

bibtex

@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},
}

	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

ritulk/MPNET-fine-tuned-political-clustering

MPNet base trained on semantic text similarity

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Semantic Similarity

Semantic Similarity

Semantic Similarity

Training Details

Training Dataset

projecte-aina/sts-ca

Evaluation Dataset

projecte-aina/sts-ca

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

CoSENTLoss

ritulk/
MPNET-fine-tuned-political-clustering