---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:12826
- loss:BatchAllTripletLoss
widget:
- source_sentence: ' '
sentences:
- ' derrick erection in situ, repairing and dismantling'
- Działalność usługowa związana z przeprowadzkami
- provision of intermediation services for the purchase of land, water or air transportation
for passengers
- source_sentence: g
sentences:
- crushing, cleaning and sorting of other waste, e
- ' students, scientists, staff, members as well as operation of government archives-
activities related to the acquisition or collection of archives'
- ' fundraising organisation services on a contract or fee basis- court reporting
or stenotype recording services- public stenography services- real-time (i'
- source_sentence: g
sentences:
- the programming may be of a general or specialised nature (e
- ), słodkich lub solonych
- composite diagnostic or laboratory reagents
- source_sentence: palliative nursing care for out-patients
sentences:
- ' pre-sorting, addressing'
- retail sale of recording media players and recorders
- manufacture of gaiters, leggings and similar articles
- source_sentence: ' this class also includes: laboratory doping control activities'
sentences:
- ' freight transport on mainline rail networks as well as short line freight railroads'
- telecommunication carrier equipment
- manufacture of toner cartridges
---
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). 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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 tokens
- **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': 128, '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})
)
```
## 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("annazdr/nace-pl-v3")
# Run inference
sentences = [
' this class also includes: laboratory doping control activities',
'manufacture of toner cartridges',
'telecommunication carrier equipment',
]
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]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 12,826 training samples
* Columns: sentence_0 and label
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| type | string | int |
| details | - min: 2 tokens
- mean: 18.09 tokens
- max: 128 tokens
| - 1: ~0.10%
- 2: ~0.30%
- 3: ~0.10%
- 4: ~0.40%
- 7: ~0.20%
- 8: ~0.10%
- 9: ~0.20%
- 10: ~0.30%
- 11: ~0.30%
- 13: ~0.30%
- 14: ~0.10%
- 18: ~0.20%
- 19: ~0.20%
- 20: ~0.10%
- 21: ~0.20%
- 22: ~0.10%
- 23: ~0.20%
- 24: ~0.30%
- 25: ~0.30%
- 26: ~0.10%
- 28: ~0.10%
- 29: ~0.10%
- 31: ~0.10%
- 32: ~0.10%
- 33: ~0.10%
- 34: ~0.20%
- 35: ~0.10%
- 37: ~0.20%
- 39: ~0.20%
- 40: ~0.40%
- 41: ~0.40%
- 42: ~0.10%
- 44: ~0.60%
- 45: ~0.10%
- 46: ~0.20%
- 48: ~0.20%
- 49: ~0.10%
- 51: ~0.10%
- 52: ~0.30%
- 54: ~0.20%
- 55: ~0.40%
- 56: ~0.10%
- 57: ~0.40%
- 58: ~0.20%
- 59: ~0.30%
- 60: ~0.20%
- 61: ~0.30%
- 62: ~0.20%
- 63: ~0.40%
- 64: ~0.60%
- 65: ~0.50%
- 66: ~1.00%
- 67: ~0.10%
- 69: ~0.50%
- 70: ~0.20%
- 71: ~0.10%
- 72: ~0.40%
- 73: ~0.60%
- 74: ~0.20%
- 75: ~0.10%
- 76: ~0.30%
- 77: ~0.10%
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- 83: ~0.50%
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- 86: ~0.30%
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- 90: ~0.60%
- 91: ~0.30%
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- 94: ~0.20%
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- 97: ~0.80%
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- 99: ~0.70%
- 100: ~0.70%
- 101: ~0.50%
- 102: ~0.60%
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- 154: ~0.60%
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- 191: ~0.60%
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- 200: ~0.30%
- 201: ~0.20%
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- 227: ~0.20%
- 228: ~0.10%
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- 230: ~0.30%
- 231: ~0.20%
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- 241: ~0.40%
- 242: ~0.10%
- 243: ~0.30%
- 244: ~0.10%
- 245: ~0.40%
- 246: ~0.10%
- 247: ~0.30%
- 249: ~0.40%
- 253: ~0.20%
- 254: ~0.10%
- 255: ~0.10%
- 256: ~0.20%
- 257: ~0.30%
- 258: ~0.10%
- 259: ~0.10%
- 260: ~0.40%
- 264: ~0.10%
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- 270: ~0.10%
- 271: ~0.30%
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- 278: ~0.30%
- 280: ~0.10%
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- 285: ~0.50%
- 286: ~0.20%
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- 298: ~0.10%
- 300: ~0.10%
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- 302: ~0.20%
- 303: ~0.30%
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- 306: ~0.30%
- 307: ~0.10%
- 308: ~0.20%
- 310: ~0.10%
- 311: ~0.30%
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- 313: ~0.20%
- 314: ~0.30%
- 315: ~0.40%
- 317: ~0.10%
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- 322: ~0.10%
- 324: ~0.60%
- 325: ~0.10%
- 327: ~0.20%
- 328: ~0.10%
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- 330: ~0.10%
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- 332: ~0.20%
- 333: ~0.10%
- 334: ~0.60%
- 335: ~0.30%
- 336: ~0.30%
- 337: ~0.30%
- 339: ~0.10%
- 341: ~0.30%
- 342: ~0.10%
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- 344: ~0.20%
- 345: ~0.10%
- 346: ~0.20%
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- 348: ~0.30%
- 349: ~0.40%
- 351: ~0.40%
- 352: ~0.10%
- 353: ~0.10%
- 354: ~0.30%
- 356: ~0.30%
- 357: ~0.10%
- 358: ~0.10%
- 359: ~0.10%
- 360: ~0.40%
- 361: ~0.30%
- 364: ~0.20%
- 366: ~0.50%
- 367: ~0.30%
- 368: ~0.40%
- 369: ~0.30%
- 370: ~0.10%
- 372: ~0.10%
- 373: ~0.30%
- 374: ~0.10%
- 375: ~0.50%
- 376: ~0.10%
- 377: ~0.40%
- 378: ~0.10%
- 379: ~0.40%
- 380: ~0.30%
- 381: ~0.30%
- 382: ~0.10%
- 383: ~0.20%
- 384: ~0.20%
- 385: ~0.40%
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- 390: ~0.30%
- 391: ~0.10%
- 392: ~0.50%
- 393: ~0.20%
- 394: ~0.10%
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- 399: ~0.30%
- 400: ~0.20%
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- 407: ~0.30%
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- 420: ~0.30%
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- 487: ~0.30%
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- 517: ~0.40%
- 519: ~0.90%
- 520: ~0.80%
- 521: ~1.00%
- 522: ~0.10%
- 523: ~0.20%
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- 525: ~0.30%
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- 548: ~0.30%
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- 550: ~0.20%
- 551: ~0.70%
- 552: ~0.30%
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- 559: ~0.40%
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- 570: ~0.20%
- 572: ~0.10%
- 573: ~0.10%
- 574: ~0.10%
- 578: ~0.30%
|
* Samples:
| sentence_0 | label |
|:---------------------------------------------------------------|:-----------------|
| ) | 306 |
| transport of freight over sea and coastal waters | 278 |
| | 319 |
* Loss: [BatchAllTripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `num_train_epochs`: 4
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_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
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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`: 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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin