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---
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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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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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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