sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:25743 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:WhereIsAI/UAE-Large-V1 base_model:finetune:WhereIsAI/UAE-Large-V1 autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: WhereIsAI/UAE-Large-V1
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:25743
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 2:00 PM Facebook ... 0.0KB/sill Arief Smansa Fadhillah Jun 9 at
    9:44 am.  89 111 60 = If in the next 2 weeks the people America who violates PSBB
    will not happen corpses scattered on the streets, then I sure that the fear of
    Corona is just a scam created by WHO and in Support by Mass Media.
  sentences:
  - MPs are entitled to a full pension after six months in office
  - Photos of anti-racism demonstrations in the United States
  - Wisconsin has more votes cast than registered voters.
- source_sentence: A religious festival in Jaffna... Radical Otulabban, who opposes
    the ordination of children, has nothing to do with this...
  sentences:
  - A genuine article on Olympic female weightlifter suffering testicle injury?
  - This video shows pilots demonstrating against Covid vaccines
  - Photo shows distressed children at a religious ritual in Sri Lanka
- source_sentence: '← 42 CHANNEL Markus Hain... * 107.4K subscribers Pinned message
    If you like my work for our freedom... 74% 22:32 KANAL Markus Haintz, Lawyer &
    Fre... forwarded message By Vicky_TheRedSparrow BREAKING NEWS: The Supreme Court
    of Justice in the United States decided that the Covid vaccination no vaccine
    is unsafe and um must be avoided at all costs - Big Pharma and Anthony Fauci have
    lost a lawsuit by Robert F. Kennedy Jr. and a group of scientists has been submitted!  /breaking-news-the-supreme-court
    -in-the-us-has-ruled-that-the-covid -pathogen-is-not-a-vaccine-is-unsafe -and-must-be-avoided-at-all-costs-big
    -pharma-and-anthony-fauci-have-lost -a-lawsuit-filed-by-r/ Truth To Power BREAKING
    NEWS: The Supreme Court In The US Has Ruled That The Covid Dathanen in Distress
    & Vanaina la Llunafn MUTE OFF X 138'
  sentences:
  - 'USA: Supreme Court rules against corona vaccinations'
  - Pakistani government appoints former army general to head medical regulatory body
  - '"In Denmark, the law obliges owners of large agricultural land to plant 5% of
    their land flowers for bees. In Portugal?"'
- source_sentence: MEXICO, Failed extortion in Celaya… and he came back to throw a
    grenade ….
  sentences:
  - Attack on people in a cafe in Celaya, Mexico
  - UNICEF issued guidelines for the prevention of coronavirus infections
  - Image shows a road in Sri Lanka
- source_sentence: The ELN movement supported with 80 thousand dollars! That is little
    money. What's wrong with that? For us, nor the FARC nor the ELN they are groups
    terrorists ” revores Arauz PRISI ANDRES ARAUZLela campaign with funds from drug
    traffickers and terrorists
  sentences:
  - Andrés Arauz said that he accepted financing from the ELN and that neither the
    ELN nor the FARC are armed groups
  - Holy communion banned in Toronto
  - Myanmar leader gives three-fingered salute in support of Thai protesters?
---

# SentenceTransformer based on WhereIsAI/UAE-Large-V1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1). It maps sentences & paragraphs to a 1024-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:** [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1) <!-- at revision f4264cd240f4e46a527f9f57a70cda6c2a12d248 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **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: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, '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': 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("sentence_transformers_model_id")
# Run inference
sentences = [
    "The ELN movement supported with 80 thousand dollars! That is little money. What's wrong with that? For us, nor the FARC nor the ELN they are groups terrorists ” revores Arauz PRISI ANDRES ARAUZLela campaign with funds from drug traffickers and terrorists",
    'Andrés Arauz said that he accepted financing from the ELN and that neither the ELN nor the FARC are armed groups',
    'Holy communion banned in Toronto',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 25,743 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                          | sentence_1                                                                         | label                                                         |
  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|
  | type    | string                                                                              | string                                                                             | float                                                         |
  | details | <ul><li>min: 2 tokens</li><li>mean: 109.01 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 18.19 tokens</li><li>max: 131 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                                                                                                                                                                                                                                                                           | sentence_1                                                                                                         | label            |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------|:-----------------|
  | <code>In the coming weeks and months, You will see the bananas with more pints of normal, due to the effect of the ashes of the volcano! Don't stop buying them! It only affects the image not the taste! Crops need to be harvested so that the banana trees can come out ahead! alamy a a alam alamy</code>        | <code>Canary bananas are going to have more spots than normal due to the effect of the ashes of the volcano</code> | <code>1.0</code> |
  | <code>Are they canceling Title of those who are over 70 years old!? Negative certificate Electoral registry office, says I owe nothing. But at the bottom of the page. it says "unsubscribed"! Over 70s must check that everything is in order with their title. Millions of retirees can vote for Bolsonaro.</code> | <code>Population over 70 is having the voter registration canceled in 2022</code>                                  | <code>1.0</code> |
  | <code>VIN dti PHILIPPINES FDA APPROVED Honey-C H52% 18:43 itine Appemess Vinity Resistance Bus KONTRA CORONA VIRUS Let's boost our immune system!</code>                                                                                                                                                             | <code>Government-approved immunity booster for COVID-19 sold online</code>                                         | <code>1.0</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`: 2
- `per_device_eval_batch_size`: 2
- `num_train_epochs`: 1
- `multi_dataset_batch_sampler`: round_robin

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 2
- `per_device_eval_batch_size`: 2
- `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
- `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
- `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`: 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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step  | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0388 | 500   | 0.0473        |
| 0.0777 | 1000  | 0.0264        |
| 0.1165 | 1500  | 0.0258        |
| 0.1554 | 2000  | 0.0322        |
| 0.1942 | 2500  | 0.0225        |
| 0.2331 | 3000  | 0.0318        |
| 0.2719 | 3500  | 0.036         |
| 0.3108 | 4000  | 0.0254        |
| 0.3496 | 4500  | 0.0166        |
| 0.3884 | 5000  | 0.0231        |
| 0.4273 | 5500  | 0.0268        |
| 0.4661 | 6000  | 0.0293        |
| 0.5050 | 6500  | 0.0315        |
| 0.5438 | 7000  | 0.0292        |
| 0.5827 | 7500  | 0.0308        |
| 0.6215 | 8000  | 0.0206        |
| 0.6603 | 8500  | 0.0329        |
| 0.6992 | 9000  | 0.0379        |
| 0.7380 | 9500  | 0.0133        |
| 0.7769 | 10000 | 0.0255        |
| 0.8157 | 10500 | 0.0138        |
| 0.8546 | 11000 | 0.0414        |
| 0.8934 | 11500 | 0.015         |
| 0.9323 | 12000 | 0.0234        |
| 0.9711 | 12500 | 0.0274        |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.1
- Tokenizers: 0.21.0

## 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}
}
```

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