This is a sentence-transformers model finetuned from intfloat/multilingual-e5-small. 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: intfloat/multilingual-e5-small
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 dimensions
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})
  (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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("helmis/e5-small-it-profiles")
# Run inference
sentences = [
    'Administrateur systèmes, stockage SAN NAS, NetApp, Pure Storage',
    'Admin sys Linux, automatisation Ansible, scripts Python bash',
    'Ingénieur data, feature store, Feast, Tecton, ML platform',
]
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

Triplet

Dataset: val-it-profiles
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9968

Training Details

Training Dataset

Unnamed Dataset

Size: 2,480 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 10 tokens
mean: 17.53 tokens
max: 27 tokens
min: 10 tokens
mean: 17.59 tokens
max: 27 tokens
min: 10 tokens
mean: 17.45 tokens
max: 27 tokens

Samples:

anchor	positive	negative
`Python engineer, messaging queues, Celery, RabbitMQ`	`Développeur Python, APIs tierces, intégrations Stripe, Twilio`	`Testeur, gestion défauts, Jira, rapports, métriques qualité`
`Senior Flutter developer, architecture propre, feature-first`	`Flutter engineer, Firebase Firestore, Auth, Storage, FCM`	`Analyste de données, Google BigQuery, requêtes SQL complexes`
`Python developer, computer vision, OpenCV, YOLO, PIL`	`Senior Python developer, async programming, asyncio, aiohttp`	`Développeur Angular, Auth0, Keycloak, OIDC, guards routes`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.EUCLIDEAN", 3 "triplet_margin": 5 4}

Evaluation Dataset

Unnamed Dataset

Size: 310 evaluation samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 310 samples:
anchor positive negative
type string string string
details
min: 10 tokens
mean: 17.57 tokens
max: 26 tokens
min: 11 tokens
mean: 17.59 tokens
max: 26 tokens
min: 11 tokens
mean: 17.39 tokens
max: 26 tokens

Samples:

anchor	positive	negative
`Angular developer, animations Angular, CDK Overlay`	`Développeur Angular, PWA, service workers, push notifications`	`Rust developer, game development, Bevy engine`
`Security engineer, application security, SAST, DAST, SCA`	`Cybersecurity engineer, threat intelligence, vulnerability management`	`Data analyst, ETL, transformation données, nettoyage, qualité`
`Analyste données, intégration sources multiples, consolidation`	`Data analyst, présentation résultats, communication non-technique`	`Java engineer with Spring Security and JWT authentication`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.EUCLIDEAN", 3 "triplet_margin": 5 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 64
learning_rate: 2e-05
num_train_epochs: 10
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True

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: 64
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: 2e-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: linear
lr_scheduler_kwargs: None
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
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: 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}
parallelism_config: 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
project: huggingface
trackio_space_id: trackio
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
hub_revision: None
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
include_tokens_per_second: False
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	val-it-profiles_cosine_accuracy
0.2564	20	4.9364	-	-
0.5128	40	4.8798	-	-
0.7692	60	4.6359	-	-
1.0	78	-	3.9695	0.9871
1.0256	80	4.2702	-	-
1.2821	100	4.0951	-	-
1.5385	120	4.0065	-	-
1.7949	140	3.937	-	-
2.0	156	-	3.7489	0.9935
2.0513	160	3.9045	-	-
2.3077	180	3.8515	-	-
2.5641	200	3.8601	-	-
2.8205	220	3.8696	-	-
3.0	234	-	3.7102	0.9935
3.0769	240	3.8469	-	-
3.3333	260	3.8189	-	-
3.5897	280	3.8238	-	-
3.8462	300	3.7987	-	-
4.0	312	-	3.6924	0.9935
4.1026	320	3.783	-	-
4.3590	340	3.7763	-	-
4.6154	360	3.773	-	-
4.8718	380	3.7956	-	-
5.0	390	-	3.6828	0.9935
5.1282	400	3.7761	-	-
5.3846	420	3.7795	-	-
5.6410	440	3.7608	-	-
5.8974	460	3.7445	-	-
6.0	468	-	3.6786	0.9935
6.1538	480	3.7554	-	-
6.4103	500	3.7717	-	-
6.6667	520	3.7639	-	-
6.9231	540	3.7349	-	-
7.0	546	-	3.6746	0.9935
7.1795	560	3.7367	-	-
7.4359	580	3.756	-	-
7.6923	600	3.7793	-	-
7.9487	620	3.716	-	-
8.0	624	-	3.6725	0.9968
8.2051	640	3.7199	-	-
8.4615	660	3.75	-	-
8.7179	680	3.7433	-	-
8.9744	700	3.756	-	-
9.0	702	-	3.6718	0.9968
9.2308	720	3.7158	-	-
9.4872	740	3.7473	-	-
9.7436	760	3.7552	-	-
10.0	780	3.7348	3.6713	0.9968

Framework Versions

Python: 3.12.13
Sentence Transformers: 3.3.1
Transformers: 4.57.6
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0
Datasets: 4.0.0
Tokenizers: 0.22.2

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",
}

TripletLoss

bibtex

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

	anchor	positive	negative
type	string	string	string
details	min: 10 tokens mean: 17.53 tokens max: 27 tokens	min: 10 tokens mean: 17.59 tokens max: 27 tokens	min: 10 tokens mean: 17.45 tokens max: 27 tokens