metadata

language:
  - de
  - en
  - es
  - fr
  - it
  - nl
  - pl
  - pt
  - ru
  - zh
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:5749
  - loss:CoSENTLoss
base_model: ymelka/camembert-cosmetic-finetuned
datasets:
  - PhilipMay/stsb_multi_mt
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
widget:
  - source_sentence: >-
      Nous nous déplaçons "... par rapport au cadre de repos cosmique en
      mouvement ... à environ 371 km/s vers la constellation du Lion".
    sentences:
      - La dame a fait frire la viande panée dans de l'huile chaude.
      - Il n'y a pas d'alambic qui ne soit pas relatif à un autre objet.
      - >-
        Le joueur de basket-ball est sur le point de marquer des points pour son
        équipe.
  - source_sentence: >-
      Le professeur Burkhauser a effectué des recherches approfondies sur les
      personnes qui sont pénalisées par l'augmentation du salaire minimum.
    sentences:
      - Un adolescent parle à une fille par le biais d'une webcam.
      - Une femme est en train de couper des oignons verts.
      - >-
        Les lois sur le salaire minimum nuisent le plus aux personnes les moins
        qualifiées et les moins productives.
  - source_sentence: >-
      Bien que le terme "reine" puisse faire référence à la fois à la reine
      régente (souveraine) ou à la reine consort, le roi a toujours été le
      souverain.
    sentences:
      - Des moutons paissent dans le champ devant une rangée d'arbres.
      - >-
        Il y a une très bonne raison de ne pas appeler le conjoint de la Reine
        "Roi" - parce qu'il n'est pas le Roi.
      - Un groupe de personnes âgées pose autour d'une table à manger.
  - source_sentence: Deux pygargues à tête blanche perchés sur une branche.
    sentences:
      - Un groupe de militaires joue dans un quintette de cuivres.
      - Deux aigles sont perchés sur une branche.
      - Un homme qui joue de la guitare sous la pluie.
  - source_sentence: Un homme joue de la guitare.
    sentences:
      - >-
        Il est possible qu'un système solaire comme le nôtre existe en dehors
        d'une galaxie.
      - Un homme joue de la flûte.
      - Un homme est en train de manger une banane.
pipeline_tag: sentence-similarity
model-index:
  - name: SentenceTransformer based on ymelka/camembert-cosmetic-finetuned
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: stsb fr dev
          type: stsb-fr-dev
        metrics:
          - type: pearson_cosine
            value: 0.6401461834329478
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6661576168424006
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7077411059971963
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7104395816607704
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.6183470655093759
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.6339424060254548
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.18614455072383299
            name: Pearson Dot
          - type: spearman_dot
            value: 0.21677402345623561
            name: Spearman Dot
          - type: pearson_max
            value: 0.7077411059971963
            name: Pearson Max
          - type: spearman_max
            value: 0.7104395816607704
            name: Spearman Max
          - type: pearson_cosine
            value: 0.834390325106948
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8564941342147334
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8518548236293758
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.854193303324745
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8541012365072966
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8555434573522197
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.4989804086580052
            name: Pearson Dot
          - type: spearman_dot
            value: 0.5094008186566353
            name: Spearman Dot
          - type: pearson_max
            value: 0.8541012365072966
            name: Pearson Max
          - type: spearman_max
            value: 0.8564941342147334
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: stsb fr test
          type: stsb-fr-test
        metrics:
          - type: pearson_cosine
            value: 0.7979696368103
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8219240068315988
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8237827107867745
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8221440625680553
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8230384709547542
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8218369251066925
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.4089365107737232
            name: Pearson Dot
          - type: spearman_dot
            value: 0.4588995887587045
            name: Spearman Dot
          - type: pearson_max
            value: 0.8237827107867745
            name: Pearson Max
          - type: spearman_max
            value: 0.8221440625680553
            name: Spearman Max

SentenceTransformer based on ymelka/camembert-cosmetic-finetuned

This is a sentence-transformers model finetuned from ymelka/camembert-cosmetic-finetuned on the PhilipMay/stsb_multi_mt 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: ymelka/camembert-cosmetic-finetuned
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- PhilipMay/stsb_multi_mt
Languages: de, en, es, fr, it, nl, pl, pt, ru, zh

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: CamembertModel 
  (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("ymelka/camembert-cosmetic-similarity")
# Run inference
sentences = [
    'Un homme joue de la guitare.',
    'Un homme est en train de manger une banane.',
    'Un homme joue de la flûte.',
]
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

Dataset: stsb-fr-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6401
spearman_cosine	0.6662
pearson_manhattan	0.7077
spearman_manhattan	0.7104
pearson_euclidean	0.6183
spearman_euclidean	0.6339
pearson_dot	0.1861
spearman_dot	0.2168
pearson_max	0.7077
spearman_max	0.7104

Semantic Similarity

Dataset: stsb-fr-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8344
spearman_cosine	0.8565
pearson_manhattan	0.8519
spearman_manhattan	0.8542
pearson_euclidean	0.8541
spearman_euclidean	0.8555
pearson_dot	0.499
spearman_dot	0.5094
pearson_max	0.8541
spearman_max	0.8565

Semantic Similarity

Dataset: stsb-fr-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.798
spearman_cosine	0.8219
pearson_manhattan	0.8238
spearman_manhattan	0.8221
pearson_euclidean	0.823
spearman_euclidean	0.8218
pearson_dot	0.4089
spearman_dot	0.4589
pearson_max	0.8238
spearman_max	0.8221

Training Details

Training Dataset

PhilipMay/stsb_multi_mt

Dataset: PhilipMay/stsb_multi_mt at 3acaa3d
Size: 5,749 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 11.1 tokens
max: 30 tokens

min: 6 tokens
mean: 11.04 tokens
max: 26 tokens

min: 0.0
mean: 2.7
max: 5.0

	sentence1	sentence2	score
type	string	string	float
details	min: 6 tokens mean: 11.1 tokens max: 30 tokens	min: 6 tokens mean: 11.04 tokens max: 26 tokens	min: 0.0 mean: 2.7 max: 5.0

Samples:

sentence1	sentence2	score
`Un avion est en train de décoller.`	`Un avion est en train de décoller.`	`5.0`
`Un homme joue d'une grande flûte.`	`Un homme joue de la flûte.`	`3.799999952316284`
`Un homme étale du fromage râpé sur une pizza.`	`Un homme étale du fromage râpé sur une pizza non cuite.`	`3.799999952316284`

Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

PhilipMay/stsb_multi_mt

Dataset: PhilipMay/stsb_multi_mt at 3acaa3d
Size: 1,500 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 17.45 tokens
max: 52 tokens

min: 6 tokens
mean: 17.35 tokens
max: 48 tokens

min: 0.0
mean: 2.36
max: 5.0

	sentence1	sentence2	score
type	string	string	float
details	min: 6 tokens mean: 17.45 tokens max: 52 tokens	min: 6 tokens mean: 17.35 tokens max: 48 tokens	min: 0.0 mean: 2.36 max: 5.0

Samples:

sentence1	sentence2	score
`Un homme avec un casque de sécurité est en train de danser.`	`Un homme portant un casque de sécurité est en train de danser.`	`5.0`
`Un jeune enfant monte à cheval.`	`Un enfant monte à cheval.`	`4.75`
`Un homme donne une souris à un serpent.`	`L'homme donne une souris au serpent.`	`5.0`

Loss: CoSENTLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-05
weight_decay: 0.01
warmup_ratio: 0.1
bf16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
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: 2e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 3
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: True
fp16: False
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: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	stsb-fr-dev_spearman_cosine	stsb-fr-test_spearman_cosine
0	0	-	-	0.6661	-
0.2778	100	4.9452	4.4417	0.7733	-
0.5556	200	4.667	4.4273	0.7986	-
0.8333	300	4.4904	4.3058	0.8338	-
1.1111	400	4.1679	4.2723	0.8491	-
1.3889	500	4.138	4.3575	0.8464	-
1.6667	600	4.5737	4.3427	0.8479	-
1.9444	700	4.3086	4.4455	0.8510	-
2.2222	800	3.8711	4.4135	0.8590	-
2.5	900	4.064	4.4775	0.8567	-
2.7778	1000	4.2255	4.4733	0.8565	-
3.0	1080	-	-	-	0.8219

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.3.0+cu121
Accelerate: 0.31.0
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},
}