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tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:290
  - loss:OnlineContrastiveLoss
base_model: intfloat/multilingual-e5-large
widget:
  - source_sentence: Antes se coge al mentiroso que al cojo
    sentences:
      - A escudero pobre, taza de plata y cántaro de cobre
      - En río revuelto, pesca abundante
      - Se ayuda primero al necesitado que al engañador.
  - source_sentence: Asno de muchos, lobos lo comen
    sentences:
      - Sabio entre sabios, amigos lo respetan.
      - El que mucho madruga más hace que el que Dios ayuda.
      - Se pilla antes a un mentiroso que a un cojo
  - source_sentence: Al buey por el asta, y al hombre por la palabra
    sentences:
      - Si no quieres arroz con leche, toma tres tazas
      - Al hombre por la palabra, y al buey por el cuerno ata
      - >-
        Ese no es tu amigo, sino alguien que siempre busca estar rodeado de
        bullicio y actividad.
  - source_sentence: Al médico, confesor y letrado, hablarles claro
    sentences:
      - Al médico, confesor y letrado, no le hayas engañado
      - Más vale a quien Dios ayuda que quien mucho madruga
      - Al que anda entre la miel, algo se le pega
  - source_sentence: A muertos y a idos, no hay amigos
    sentences:
      - Al buen callar llaman santo
      - A los vivos y presentes, siempre hay amigos.
      - Al que de prestado se viste, en la calle lo desnudan
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on intfloat/multilingual-e5-large
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: pearson_cosine
            value: 0.8334934833047165
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8261353280714282
            name: Spearman Cosine

SentenceTransformer based on intfloat/multilingual-e5-large

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large on the csv dataset. It maps sentences & paragraphs to a 1024-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: intfloat/multilingual-e5-large
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- csv

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: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
  (2): Normalize()
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'A muertos y a idos, no hay amigos',
    'A los vivos y presentes, siempre hay amigos.',
    'Al buen callar llaman santo',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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	0.8335
spearman_cosine	0.8261

Training Details

Training Dataset

csv

Dataset: csv
Size: 290 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 290 samples:
sentence1 sentence2 label
type string string int
details
min: 7 tokens
mean: 11.68 tokens
max: 22 tokens

min: 7 tokens
mean: 17.01 tokens
max: 44 tokens

0: ~50.00%
1: ~50.00%

	sentence1	sentence2	label
type	string	string	int
details	min: 7 tokens mean: 11.68 tokens max: 22 tokens	min: 7 tokens mean: 17.01 tokens max: 44 tokens	0: ~50.00% 1: ~50.00%

Samples:

sentence1	sentence2	label
`Gota a gota, la mar se agota.`	`Con el pasar del tiempo se llega a alcanzar cualquier meta.`	`1`
`Dime de qué presumes y te diré de qué careces.`	`Dime de qué careces y te diré de qué dispones.`	`0`
`Cómo se vive, se muere.`	`De aquella forma que hemos vivido nuestra vida será de la forma en la que moriremos.`	`1`

Loss: OnlineContrastiveLoss

Evaluation Dataset

Unnamed Dataset

Size: 1,006 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 7 tokens
mean: 12.51 tokens
max: 25 tokens

min: 6 tokens
mean: 14.82 tokens
max: 38 tokens

0: ~49.70%
1: ~50.30%

	sentence1	sentence2	label
type	string	string	int
details	min: 7 tokens mean: 12.51 tokens max: 25 tokens	min: 6 tokens mean: 14.82 tokens max: 38 tokens	0: ~49.70% 1: ~50.30%

Samples:

sentence1	sentence2	label
`¿Adónde irá el buey que no are?`	`¿A dó irá el buey que no are?`	`1`
`¿Adónde irá el buey que no are?`	`¿Adónde irá el buey que no are ni la mula que no cargue?`	`1`
`¿Adónde irá el buey que no are?`	`¿Adónde irá el buey que no are, sino al matadero?`	`1`

Loss: OnlineContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
learning_rate: 1e-05
num_train_epochs: 4
lr_scheduler_type: constant
load_best_model_at_end: True
eval_on_start: 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: 8
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 1e-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: 4
max_steps: -1
lr_scheduler_type: constant
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: 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: 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}
tp_size: 0
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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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: True
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	spearman_cosine
0	0	-	0.1095	0.7843
0.1351	5	0.6784	0.0765	0.8123
0.2703	10	0.5088	0.0533	0.8303
0.4054	15	0.4364	0.0475	0.8339
0.5405	20	0.3456	0.0435	0.8345
0.6757	25	0.1423	0.0424	0.8324
0.8108	30	0.2852	0.0443	0.8271
0.9459	35	0.2616	0.0514	0.8262
1.0811	40	0.1451	0.0521	0.8232
1.2162	45	0.2046	0.0496	0.8221
1.3514	50	0.055	0.0516	0.8197
1.4865	55	0.0956	0.0545	0.8190
1.6216	60	0.1213	0.0533	0.8213
1.7568	65	0.2378	0.0464	0.8253
1.8919	70	0.2723	0.0458	0.8249
2.0270	75	0.0603	0.0467	0.8226
2.1622	80	0.1089	0.0415	0.8263
2.2973	85	0.0813	0.0417	0.8270
2.4324	90	0.0	0.0437	0.8250
2.5676	95	0.0436	0.0467	0.8242
2.7027	100	0.0	0.0451	0.8242
2.8378	105	0.0	0.0451	0.8243
2.9730	110	0.0271	0.0433	0.8243
3.1081	115	0.007	0.0502	0.8195
3.2432	120	0.1025	0.0523	0.8195
3.3784	125	0.1244	0.0527	0.8251
3.5135	130	0.0	0.0534	0.8262
3.6486	135	0.0259	0.0571	0.8262
3.7838	140	0.0939	0.0526	0.8273
3.9189	145	0.1038	0.0527	0.8261

Framework Versions

Python: 3.12.9
Sentence Transformers: 3.4.1
Transformers: 4.50.0
PyTorch: 2.6.0+cpu
Accelerate: 1.6.0
Datasets: 3.5.0
Tokenizers: 0.21.1

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",
}