---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:152913
- loss:BatchAllTripletLoss
base_model: Almawave/Velvet-2B
widget:
- source_sentence: La crisi non tocca il mercato del “luxury food”, che continua a
crescere
sentences:
- Avviso aggiornamento (1.55)
- Il fotoritocco sui social per fingere di essere al Mic
- Turchia, Biberovic giocherà da passaportato nella finestra FIBA
- source_sentence: 'Miller stagione finita, Pagani: «Rimpiazzo? Valutiamo il mercato
europeo»'
sentences:
- Beautiful, le trame della settimana dal 2 al 7 dicembre
- 'Corte dei conti: concorso per 8 funzionari a tempo indeterminato'
- 'Leonardo, AD: incontrato stamani numero uno Airbus su alleanza satellitare'
- source_sentence: 'Il segreto di Jalen Hurts in una foto sullo smartphone: così ha
vinto e sconfitto gli scettici'
sentences:
- Scarcerato il boss Ernesto Fazzalari, era il latitante più ricercato dopo Messina
Denaro
- 'UniVdA: ecco il nuovo master in Psicologia dello sport'
- 'Gran Turismo 7: arrivano quattro nuove auto con l’update 1.55 [VIDEO]'
- source_sentence: 'San Vito al Torre, recuperato un antico monumento funerario romano
dal fiume: la scoperta'
sentences:
- Charlotte Casiraghi in pubblico dopo le voci di divorzio
- Mr. Bezos, la sua non è imparzialità ma viltà
- 'Elisa Di Francisca a La Talpa: “Sono troppo vera per tenere segreti”'
- source_sentence: 'NBA, dopo l’addio a Schroeder i Nets promuovono Simmons in quintetto:
l''idea è correre'
sentences:
- 'Picasso a Milano: al Mudec la mostra sulle metamorfosi del maestro'
- Italia Viva e +Europa non parteciperanno alle elezioni regionali in Liguria
- 'Achille Costacurta rivela: «Sono stato rinchiuso per un anno e sette mesi in
un centro penale'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- average_precision
- f1
- precision
- recall
- threshold
model-index:
- name: SentenceTransformer based on Almawave/Velvet-2B
results:
- task:
type: paraphrase-mining
name: Paraphrase Mining
dataset:
name: Unknown
type: unknown
metrics:
- type: average_precision
value: 0.5283532795784699
name: Average Precision
- type: f1
value: 0.5502357974952371
name: F1
- type: precision
value: 0.5567564151181899
name: Precision
- type: recall
value: 0.5438661480521084
name: Recall
- type: threshold
value: 0.9310455322265625
name: Threshold
---
# SentenceTransformer based on Almawave/Velvet-2B
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Almawave/Velvet-2B](https://huggingface.co/Almawave/Velvet-2B). It maps sentences & paragraphs to a 2048-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:** [Almawave/Velvet-2B](https://huggingface.co/Almawave/Velvet-2B)
- **Maximum Sequence Length:** 32768 tokens
- **Output Dimensionality:** 2048 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': 32768, 'do_lower_case': False}) with Transformer model: MistralModel
(1): Pooling({'word_embedding_dimension': 2048, 'pooling_mode_cls_token': False, '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': True, '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("ancc/Velvet-2B-embedding-news")
# Run inference
sentences = [
"NBA, dopo l’addio a Schroeder i Nets promuovono Simmons in quintetto: l'idea è correre",
'Italia Viva e +Europa non parteciperanno alle elezioni regionali in Liguria',
'Achille Costacurta rivela: «Sono stato rinchiuso per un anno e sette mesi in un centro penale',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 2048]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Paraphrase Mining
* Evaluated with [ParaphraseMiningEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator)
| Metric | Value |
|:----------------------|:-----------|
| **average_precision** | **0.5284** |
| f1 | 0.5502 |
| precision | 0.5568 |
| recall | 0.5439 |
| threshold | 0.931 |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 152,913 training samples
* Columns: sentence and label
* Approximate statistics based on the first 1000 samples:
| | sentence | label |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| type | string | int |
| details | - min: 3 tokens
- mean: 19.14 tokens
- max: 47 tokens
| - 0: ~0.50%
- 1: ~0.50%
- 2: ~0.50%
- 3: ~0.50%
- 4: ~0.50%
- 5: ~0.20%
- 6: ~0.50%
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- 22: ~0.30%
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|
* Samples:
| sentence | label |
|:-------------------------------------------------------------------------------------------|:---------------|
| MERCATO LBA - Treviso, Giofrè: "Mercato in continua osservazione, vedremo..." | 0 |
| Ky Bowman: Non sono soddisfatto delle mie performance | 0 |
| LBA - Treviso, Giofrè: "Sabato la Reggiana, dobbiamo vincere. Punto" | 0 |
* Loss: [BatchAllTripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)
### Evaluation Dataset
#### Unnamed Dataset
* Size: 9,310 evaluation samples
* Columns: sentence and label
* Approximate statistics based on the first 1000 samples:
| | sentence | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| type | string | int |
| details | - min: 4 tokens
- mean: 19.04 tokens
- max: 78 tokens
| - 0: ~0.50%
- 1: ~0.30%
- 2: ~0.50%
- 4: ~0.50%
- 5: ~0.20%
- 6: ~0.30%
- 7: ~0.50%
- 8: ~0.50%
- 9: ~0.50%
- 10: ~0.50%
- 12: ~0.40%
- 13: ~0.30%
- 14: ~0.30%
- 15: ~0.40%
- 16: ~0.20%
- 17: ~0.50%
- 18: ~0.40%
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|
* Samples:
| sentence | label |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| Supplenze: come funzionano i contratti fino al 31 dicembre 2024 e il calcolo del punteggio? | 0 |
| Docente non abilitato assunto a tempo determinato da concorso PNRR1: in quale scuola "andrò a finire" se nella mia si perde un posto? | 0 |
| Docenti non abilitati nominati dopo il 31 agosto da graduatorie pubblicate prima: otterranno sede di titolarità all’esito delle operazioni di mobilità [Chiarimenti] | 0 |
* Loss: [BatchAllTripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 32
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.25
- `seed`: 17
- `data_seed`: 17
- `bf16`: True
- `batch_sampler`: group_by_label
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `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`: 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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.25
- `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`: 17
- `data_seed`: 17
- `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`: 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`: group_by_label
- `multi_dataset_batch_sampler`: proportional