---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:25310
- loss:CosineSimilarityLoss
base_model: Snowflake/snowflake-arctic-embed-s
widget:
- source_sentence: encryption algorithms for mobile transactions
sentences:
- equipaggiamento per sport acquatici
- finanziamenti a lungo termine per privati
- encryption algorithms for mobile banking
- source_sentence: tecnologie di liofilizzazione per frutta e verdura
sentences:
- serbatoi di fermentazione in acciaio inox per cantine
- impianti di liofilizzazione per frutta e verdura
- medical cannulas
- source_sentence: servizi di installazione di cavi sottomarini
sentences:
- servizi di installazione di cavi sottomarini
- custom spinal fusion implants
- soluzioni disinfettanti per il settore sanitario
- source_sentence: antifouling paint for yachts
sentences:
- sistemi di ventilazione con controllo umidità integrato
- robot per la movimentazione interna
- vernici per automobili
- source_sentence: materiali isolanti per sistemi radianti a soffitto
sentences:
- Produzione di contenuti per social media nel settore moda.
- privacy and data protection training
- materiali isolanti per edifici
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- cosine_accuracy
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-s
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: custom dataset
type: custom_dataset
metrics:
- type: pearson_cosine
value: 0.7037099269944034
name: Pearson Cosine
- type: spearman_cosine
value: 0.7286991662955787
name: Spearman Cosine
- task:
type: triplet
name: Triplet
dataset:
name: all nli dataset
type: all_nli_dataset
metrics:
- type: cosine_accuracy
value: 0.8162614107131958
name: Cosine Accuracy
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsbenchmark
type: stsbenchmark
metrics:
- type: pearson_cosine
value: 0.7477235986007352
name: Pearson Cosine
- type: spearman_cosine
value: 0.7431995961099886
name: Spearman Cosine
---
# SentenceTransformer based on Snowflake/snowflake-arctic-embed-s
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-s](https://huggingface.co/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](https://huggingface.co/Snowflake/snowflake-arctic-embed-s)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
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("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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"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