---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:TripletLoss
base_model: FacebookAI/xlm-roberta-base
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
widget:
- source_sentence: Skip
  sentences:
  - Ships
  - Kapital akcyjny
  - Other finance income
- source_sentence: IIII
  sentences:
  - iii
  - Gauti dividendai
  - Loans given
- source_sentence: IVE
  sentences:
  - HH
  - Koszty finansowe
  - Current borrowings
- source_sentence: K K
  sentences:
  - TOTAL ACTIF
  - Nuomos mokejimai
  - Accruals
- source_sentence: Sales
  sentences:
  - Revenue
  - Operating profit
  - Current borrowings
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on FacebookAI/xlm-roberta-base
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy
      value: 0.9987885552019722
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.001529316610921369
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.9975135360413657
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.9990958312877694
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.9990958312877694
      name: Max Accuracy
---

# SentenceTransformer based on FacebookAI/xlm-roberta-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base). It maps sentences & paragraphs to a 768-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:** [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) <!-- at revision e73636d4f797dec63c3081bb6ed5c7b0bb3f2089 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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("slimaneMakh/triplet_CloseHlabel_farLabel_andnegativ-1M-5eps-XLMR_29may")
# Run inference
sentences = [
    'Sales',
    'Revenue',
    'Operating profit',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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## Evaluation

### Metrics

#### Triplet

* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| cosine_accuracy    | 0.9988     |
| dot_accuracy       | 0.0015     |
| manhattan_accuracy | 0.9975     |
| euclidean_accuracy | 0.9991     |
| **max_accuracy**   | **0.9991** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 660,643 training samples
* Columns: <code>anchor_label</code>, <code>pos_hlabel</code>, and <code>neg_hlabel</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor_label                                                                      | pos_hlabel                                                                       | neg_hlabel                                                                       |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                           |
  | details | <ul><li>min: 3 tokens</li><li>mean: 11.86 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.06 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.99 tokens</li><li>max: 25 tokens</li></ul> |
* Samples:
  | anchor_label                                 | pos_hlabel                                 | neg_hlabel                                                                    |
  |:---------------------------------------------|:-------------------------------------------|:------------------------------------------------------------------------------|
  | <code>Basic earnings (loss) per share</code> | <code>Tavakasum kahjum aktsia kohta</code> | <code>II Kapital z nadwyzki wartosci emisyjnej ponad wartosc nominalna</code> |
  | <code>Comprehensive income</code>            | <code>Suma dochodow calkowitych</code>     | <code>dont Marques</code>                                                     |
  | <code>Cash and cash equivalents</code>       | <code>Cash and cash equivalents</code>     | <code>Cars incl prepayments</code>                                            |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 283,133 evaluation samples
* Columns: <code>anchor_label</code>, <code>pos_hlabel</code>, and <code>neg_hlabel</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor_label                                                                      | pos_hlabel                                                                       | neg_hlabel                                                                       |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                           |
  | details | <ul><li>min: 3 tokens</li><li>mean: 11.78 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.22 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.12 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
  | anchor_label                                                                    | pos_hlabel                                             | neg_hlabel                           |
  |:--------------------------------------------------------------------------------|:-------------------------------------------------------|:-------------------------------------|
  | <code>Deferred tax assets</code>                                                | <code>Deferred tax assets</code>                       | <code>Immateriella tillgangar</code> |
  | <code>Equity</code>                                                             | <code>EGET KAPITAL inklusive periodens resultat</code> | <code>Materials</code>               |
  | <code>Adjustments for decrease (increase) in other operating receivables</code> | <code>Okning av ovriga rorelsetillgangar</code>        | <code>Rorelseresultat</code>         |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `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
- `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`: 1
- `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
- `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}
- `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
- `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_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step  | Training Loss | loss   | max_accuracy |
|:------:|:-----:|:-------------:|:------:|:------------:|
| 0.0121 | 500   | 3.7705        | -      | -            |
| 0.0242 | 1000  | 1.4084        | -      | -            |
| 0.0363 | 1500  | 0.7062        | -      | -            |
| 0.0484 | 2000  | 0.5236        | -      | -            |
| 0.0605 | 2500  | 0.4348        | -      | -            |
| 0.0727 | 3000  | 0.3657        | -      | -            |
| 0.0848 | 3500  | 0.3657        | -      | -            |
| 0.0969 | 4000  | 0.2952        | -      | -            |
| 0.1090 | 4500  | 0.3805        | -      | -            |
| 0.1211 | 5000  | 0.3255        | -      | -            |
| 0.1332 | 5500  | 0.2621        | -      | -            |
| 0.1453 | 6000  | 0.2377        | -      | -            |
| 0.1574 | 6500  | 0.2139        | -      | -            |
| 0.1695 | 7000  | 0.2085        | -      | -            |
| 0.1816 | 7500  | 0.1809        | -      | -            |
| 0.1937 | 8000  | 0.1711        | -      | -            |
| 0.2059 | 8500  | 0.1608        | -      | -            |
| 0.2180 | 9000  | 0.1808        | -      | -            |
| 0.2301 | 9500  | 0.1553        | -      | -            |
| 0.2422 | 10000 | 0.1417        | -      | -            |
| 0.2543 | 10500 | 0.1329        | -      | -            |
| 0.2664 | 11000 | 0.1689        | -      | -            |
| 0.2785 | 11500 | 0.1292        | -      | -            |
| 0.2906 | 12000 | 0.1181        | -      | -            |
| 0.3027 | 12500 | 0.1223        | -      | -            |
| 0.3148 | 13000 | 0.129         | -      | -            |
| 0.3269 | 13500 | 0.0911        | -      | -            |
| 0.3391 | 14000 | 0.113         | -      | -            |
| 0.3512 | 14500 | 0.0955        | -      | -            |
| 0.3633 | 15000 | 0.108         | -      | -            |
| 0.3754 | 15500 | 0.094         | -      | -            |
| 0.3875 | 16000 | 0.0947        | -      | -            |
| 0.3996 | 16500 | 0.0748        | -      | -            |
| 0.4117 | 17000 | 0.0699        | -      | -            |
| 0.4238 | 17500 | 0.0707        | -      | -            |
| 0.4359 | 18000 | 0.0768        | -      | -            |
| 0.4480 | 18500 | 0.0805        | -      | -            |
| 0.4601 | 19000 | 0.0705        | -      | -            |
| 0.4723 | 19500 | 0.069         | -      | -            |
| 0.4844 | 20000 | 0.072         | -      | -            |
| 0.4965 | 20500 | 0.0669        | -      | -            |
| 0.5086 | 21000 | 0.066         | -      | -            |
| 0.5207 | 21500 | 0.0624        | -      | -            |
| 0.5328 | 22000 | 0.0687        | -      | -            |
| 0.5449 | 22500 | 0.076         | -      | -            |
| 0.5570 | 23000 | 0.0563        | -      | -            |
| 0.5691 | 23500 | 0.0594        | -      | -            |
| 0.5812 | 24000 | 0.0524        | -      | -            |
| 0.5933 | 24500 | 0.0528        | -      | -            |
| 0.6055 | 25000 | 0.0448        | -      | -            |
| 0.6176 | 25500 | 0.041         | -      | -            |
| 0.6297 | 26000 | 0.0397        | -      | -            |
| 0.6418 | 26500 | 0.0489        | -      | -            |
| 0.6539 | 27000 | 0.0595        | -      | -            |
| 0.6660 | 27500 | 0.034         | -      | -            |
| 0.6781 | 28000 | 0.0569        | -      | -            |
| 0.6902 | 28500 | 0.0467        | -      | -            |
| 0.7023 | 29000 | 0.0323        | -      | -            |
| 0.7144 | 29500 | 0.0428        | -      | -            |
| 0.7266 | 30000 | 0.0344        | -      | -            |
| 0.7387 | 30500 | 0.029         | -      | -            |
| 0.7508 | 31000 | 0.0418        | -      | -            |
| 0.7629 | 31500 | 0.0285        | -      | -            |
| 0.7750 | 32000 | 0.0425        | -      | -            |
| 0.7871 | 32500 | 0.0266        | -      | -            |
| 0.7992 | 33000 | 0.0325        | -      | -            |
| 0.8113 | 33500 | 0.0215        | -      | -            |
| 0.8234 | 34000 | 0.0316        | -      | -            |
| 0.8355 | 34500 | 0.0286        | -      | -            |
| 0.8476 | 35000 | 0.0285        | -      | -            |
| 0.8598 | 35500 | 0.0284        | -      | -            |
| 0.8719 | 36000 | 0.0147        | -      | -            |
| 0.8840 | 36500 | 0.0217        | -      | -            |
| 0.8961 | 37000 | 0.0311        | -      | -            |
| 0.9082 | 37500 | 0.0202        | -      | -            |
| 0.9203 | 38000 | 0.0236        | -      | -            |
| 0.9324 | 38500 | 0.0201        | -      | -            |
| 0.9445 | 39000 | 0.0246        | -      | -            |
| 0.9566 | 39500 | 0.0177        | -      | -            |
| 0.9687 | 40000 | 0.0173        | -      | -            |
| 0.9808 | 40500 | 0.0202        | -      | -            |
| 0.9930 | 41000 | 0.017         | -      | -            |
| 1.0    | 41291 | -             | 0.0140 | 0.9991       |


### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.0
- Transformers: 4.39.3
- PyTorch: 2.1.2
- Accelerate: 0.28.0
- Datasets: 2.18.0
- Tokenizers: 0.15.2

## 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}
}
```

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