---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:MultipleNegativesRankingLoss
base_model: FacebookAI/xlm-roberta-base
widget:
- source_sentence: who did ezra play for in the nfl
  sentences:
  - how many all nba first teams does kobe have
  - who does the voice of the little mermaid
  - dont come around here no more video director
- source_sentence: who led the elves at helm s deep
  sentences:
  - who was the captain of the flying dutchman
  - what are the 2 seasons in the philippines
  - when can you get a tattoo in georgia
- source_sentence: who plays red on once upon a time
  sentences:
  - who plays the new receptionist on the office
  - who wrote the magic school bus theme song
  - when did south africa declare war on germany
- source_sentence: who plays the dark elf in thor 2
  sentences:
  - who plays mantis in guardian of the galaxy 2
  - where in los angeles do the chargers play
  - when did alaska become part of the us
- source_sentence: who plays oz in the wizard of oz
  sentences:
  - where did the wizard of oz come from
  - when did brazil win the soccer world cup
  - when did the ar 15 first go on sale
pipeline_tag: sentence-similarity
---

# 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("Stern5497/nir-2024-xlm-roberta-base")
# Run inference
sentences = [
    'who plays oz in the wizard of oz',
    'where did the wizard of oz come from',
    'when did brazil win the soccer world cup',
]
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]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 164,848 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                         | sentence_1                                                                            | sentence_2                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                                | string                                                                                |
  | details | <ul><li>min: 10 tokens</li><li>mean: 13.41 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 136 tokens</li><li>mean: 164.07 tokens</li><li>max: 239 tokens</li></ul> | <ul><li>min: 133 tokens</li><li>mean: 165.13 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
  | sentence_0                                                         | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | sentence_2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
  |:-------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>who wrote treat you better by shawn mendes</code>            | <code>{'title': '', 'text': 'Treat You Better "Treat You Better" is a song recorded by Canadian singer and songwriter Shawn Mendes. It was co-written by Mendes with Teddy Geiger, and Scott Harris. It was released on June 3, 2016 through Island Records as the lead single from his second studio album, "Illuminate" (2016). The music video was released on July 12, 2016 and features a storyline about an abusive relationship. The song peaked at number six on the US "Billboard" Hot 100, making it Mendes\' second top 10 single. In Canada, the song has peaked at number seven on the Canadian Hot 100. The'}</code>                                     | <code>{'title': '', 'text': 'Scott Harris (songwriter) Scott Harris Friedman is an American multi-platinum, Grammy nominated songwriter, producer, and musician best known for his work with Shawn Mendes and co-writing Grammy winning song, "Don\'t Let Me Down" by The Chainsmokers featuring Daya, which reached #1 on the US Mainstream Top 40 chart in 2016. Harris has most recently written 13 songs on the self-titled third album Shawn Mendes (album), which debuted at #1 on the Billboard 200 chart, in addition to 10 songs on Shawn Mendes\' sophomore album "Illuminate" including the lead single "Treat You Better" which reached the top 3 at the US'}</code>                                                       |
  | <code>where is the tanami desert located in australia</code>       | <code>{'title': '', 'text': 'zone. Tanami Desert The Tanami Desert is a desert in northern Australia situated in the Northern Territory and Western Australia. It has a rocky terrain with small hills. The Tanami was the Northern Territory\'s final frontier and was not fully explored by Australians of European descent until well into the twentieth century. It is traversed by the Tanami Track. The name "Tanami" is thought to be a corruption of the Walpiri name for the area, "Chanamee", meaning "never die". This referred to certain rock holes in the desert which were said never to run dry. Under the name "Tanami", the'}</code>                 | <code>{'title': '', 'text': '("glomerata") is from the Latin "glomeratus", meaning "heaped" or "form into a ball". Desert tea-tree occurs in the arid parts of Australia including the far north west of New South Wales, South Australia including the Flinders Ranges, the Northern Territory and Western Australia. In the latter state it has been recorded from the Carnarvon, Central Kimberley, Central Ranges, Dampierland, Gascoyne, Gibson Desert, Great Sandy Desert, Great Victoria Desert, Little Sandy Desert, Murchison, Ord Victoria Plain, Pilbara and Tanami biogeographic areas. It grows in red sand, clay and sandy loam in rocky river beds, shallow depressions and sandy flats. "Melaleuca globifera"'}</code> |
  | <code>who won the us open men s and women s singles in 2017</code> | <code>{'title': '', 'text': "that ended his season, while Kerber lost in the first round to Naomi Osaka. The men's singles tournament concluded with Rafael Nadal defeating Kevin Anderson in the final, while the women's singles tournament concluded with Sloane Stephens defeating Madison Keys in the final. The 2017 US Open was the 137th edition of the tournament and took place at the USTA Billie Jean King National Tennis Center in Flushing Meadows–Corona Park of Queens in New York City, New York, United States. The tournament was held on 14 DecoTurf hard courts. The tournament was an event run by the International Tennis Federation"}</code> | <code>{'title': '', 'text': "2017 US Open – Women's Singles Angelique Kerber was the defending champion, but was defeated in the first round by Naomi Osaka. Kerber became the second US Open defending champion to lose in the first round after Svetlana Kuznetsova in 2005. Sloane Stephens won her first Grand Slam title, defeating Madison Keys in the final, 6–3, 6–0. It was the first all-American women's final at the US Open since 2002, and the second time in three years that the final featured two first-time Grand Slam singles finalists from the same country. Stephens became the second unseeded woman in the Open"}</code>                                                                                      |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `fp16`: True
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: round_robin

#### 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
- `num_train_epochs`: 1
- `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
- `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, '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_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step  | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0485 | 500   | 1.6163        |
| 0.0971 | 1000  | 0.8086        |
| 0.1456 | 1500  | 0.6766        |
| 0.1941 | 2000  | 0.6124        |
| 0.2426 | 2500  | 0.5374        |
| 0.2912 | 3000  | 0.5115        |
| 0.3397 | 3500  | 0.4823        |
| 0.3882 | 4000  | 0.4268        |
| 0.4368 | 4500  | 0.422         |
| 0.4853 | 5000  | 0.4014        |
| 0.5338 | 5500  | 0.3765        |
| 0.5824 | 6000  | 0.3689        |
| 0.6309 | 6500  | 0.3551        |
| 0.6794 | 7000  | 0.3359        |
| 0.7279 | 7500  | 0.326         |
| 0.7765 | 8000  | 0.3158        |
| 0.8250 | 8500  | 0.2945        |
| 0.8735 | 9000  | 0.2836        |
| 0.9221 | 9500  | 0.3043        |
| 0.9706 | 10000 | 0.2761        |
| 1.0    | 10303 | -             |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.0
- Transformers: 4.40.2
- PyTorch: 2.3.0+cu118
- Accelerate: 0.29.3
- Datasets: 2.19.0
- Tokenizers: 0.19.1

## 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->