LucaZilli/model-snowflake-s_20250226_145351_finalmodel

SentenceTransformer based on Snowflake/snowflake-arctic-embed-s

This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-s. 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: Snowflake/snowflake-arctic-embed-s
• 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': True, 'pooling_mode_mean_tokens': False, '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("LucaZilli/model-snowflake-s_20250226_145351_finalmodel")
# Run inference
sentences = [
    'materiali isolanti per sistemi radianti a soffitto',
    'materiali isolanti per edifici',
    'privacy and data protection training',
]
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

Semantic Similarity

• Datasets: custom_dataset and stsbenchmark
• Evaluated with EmbeddingSimilarityEvaluator

Metric	custom_dataset	stsbenchmark
pearson_cosine	0.7037	0.7477
spearman_cosine	0.7287	0.7432

Triplet

• Dataset: all_nli_dataset
• Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.8163

Training Details

Training Dataset

Unnamed Dataset

• Size: 25,310 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 4 tokens mean: 13.32 tokens max: 31 tokens	min: 4 tokens mean: 11.06 tokens max: 31 tokens	min: 0.0 mean: 0.49 max: 1.0

• Samples:

  sentence1	sentence2	score
  ottimizzazione dei tempi di produzione per capi sartoriali di lusso	strumenti per l'ottimizzazione dei tempi di produzione	0.6
  software di programmazione robotica per lucidatura	software gestionale generico	0.4
  rete di sensori per l'analisi del suolo in tempo reale	software per gestione aziendale	0.0

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 3,164 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 5 tokens mean: 13.61 tokens max: 31 tokens	min: 4 tokens mean: 11.39 tokens max: 27 tokens	min: 0.0 mean: 0.49 max: 1.0

• Samples:

  sentence1	sentence2	score
  ispezioni regolari per camion aziendali	ispezioni regolari per camion di consegna	1.0
  blister packaging machines GMP compliant	food packaging machines	0.4
  EMI shielding paints for electronics	Vernici per schermatura elettromagnetica dispositivi elettronici	0.8

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 5
• warmup_ratio: 0.1
• fp16: 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
• torch_empty_cache_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: 5
• 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: 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: False
• 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	custom_dataset_spearman_cosine	all_nli_dataset_cosine_accuracy	stsbenchmark_spearman_cosine
-1	-1	-	-	0.7287	0.8163	0.7432
0.1264	200	0.0671	0.0434	-	-	-
0.2528	400	0.0401	0.0344	-	-	-
0.3793	600	0.0342	0.0307	-	-	-
0.5057	800	0.0347	0.0327	-	-	-
0.6321	1000	0.0322	0.0287	-	-	-
0.7585	1200	0.032	0.0279	-	-	-
0.8850	1400	0.0307	0.0282	-	-	-
1.0114	1600	0.0267	0.0279	-	-	-
1.1378	1800	0.0244	0.0266	-	-	-
1.2642	2000	0.0227	0.0282	-	-	-
1.3906	2200	0.0237	0.0249	-	-	-
1.5171	2400	0.0222	0.0273	-	-	-
1.6435	2600	0.0235	0.0246	-	-	-
1.7699	2800	0.0228	0.0247	-	-	-
1.8963	3000	0.0225	0.0241	-	-	-
2.0228	3200	0.0213	0.0244	-	-	-
2.1492	3400	0.0169	0.0234	-	-	-
2.2756	3600	0.0178	0.0257	-	-	-
2.4020	3800	0.018	0.0236	-	-	-
2.5284	4000	0.0177	0.0230	-	-	-
2.6549	4200	0.0176	0.0234	-	-	-
2.7813	4400	0.0182	0.0229	-	-	-
2.9077	4600	0.0173	0.0221	-	-	-
3.0341	4800	0.0157	0.0232	-	-	-
3.1606	5000	0.0139	0.0225	-	-	-
3.2870	5200	0.0137	0.0222	-	-	-
3.4134	5400	0.0142	0.0224	-	-	-
3.5398	5600	0.0143	0.0224	-	-	-
3.6662	5800	0.0135	0.0225	-	-	-
3.7927	6000	0.0143	0.0223	-	-	-
3.9191	6200	0.0143	0.0234	-	-	-
4.0455	6400	0.0128	0.0219	-	-	-
4.1719	6600	0.0117	0.0222	-	-	-
4.2984	6800	0.0113	0.0217	-	-	-
4.4248	7000	0.0115	0.0220	-	-	-
4.5512	7200	0.012	0.0217	-	-	-
4.6776	7400	0.0113	0.0221	-	-	-
4.8040	7600	0.012	0.0217	-	-	-
4.9305	7800	0.0105	0.0217	-	-	-

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.48.3
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.3.2
• Tokenizers: 0.21.0

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",
}