sentence-transformers safetensors new sentence-similarity feature-extraction generated_from_trainer dataset_size:25743 loss:MultipleNegativesRankingLoss custom_code arxiv:1908.10084 arxiv:1705.00652 base_model:Alibaba-NLP/gte-multilingual-base base_model:finetune:Alibaba-NLP/gte-multilingual-base autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: Alibaba-NLP/gte-multilingual-base
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: م الحين SHIA WAVES ENGLISH Indians throw thousands of idols on
    the street for not protecting them against the virus  |Many people in India have
    thrown away statues of gods and blamed why the gods with infinite power cannot
    protect them from the ravages of the coronavirus? All sects must be repositioned,
    otherwise there will be a bigger crisis in each sect. . . I don't know, when will
    it be our country's turn? you say?
  sentences:
  - Esta mulher sofreu uma convulsão após ser vacinada contra a covid-19 na Argentina
  - Images of Hindu idols destroyed for not protecting Indian people during the Covid-19
    pandemic
  - Forces raid a house in Indian-administered Kashmir
- source_sentence: 'En el mismo cuerpo legal atacaremos la raíz del problema: los
    jefes de las mafias. Tipificaremos el nuevo delito de “autoría por dominio de
    organización”. Es decir: los jefes de las bandas pagarán también por los delitos
    que ordenen cometer a sus cómplices.'
  sentences:
  - Walmart va demander une preuve de vaccination à ses clients canadiens
  - Vídeo mostra fraude de mortes na pandemia de Covid-19
  - La autoría por dominio de organización sería un nuevo delito en Ecuador
- source_sentence: Winning
  sentences:
  - President Donald Trump has 232 electoral votes, Joe Biden has 212, 226 or 227.
  - Suspected drunk drivers automatically face one month in jail under new law in
    Thailand?
  - Le bilan des violences post-électorales à M'Batto a atteint au moins une trentaine
    de morts
- source_sentence: Pablo Iglesias  Iglesias_ No soy partidario de la violencia pero
    disfrutaría viendo como matan a tiros a los líderes del PP. La derecha debe ser
    exterminada como un virus. 11:26 AM 24 ene. 12 1.682 Retweets 2.069 Likes 27 go
  sentences:
  - Pablo Iglesias tuiteó que disfrutaría de ver como matan de un tiro a líderes del
    PP y a la derecha española habría que exterminarla como a un virus
  - Delfines en un puerto de España durante el confinamiento
  - Jenazah korban virus corona di Rusia
- source_sentence: 'ليس داعشياً من بيده المسدس ..انه جندي فرنسي ينفذ اعدامات بحق مواطنين
    عزل في الجزائر !!! لم يكن حينها لا تنظيم قاعدة ولا دولة اسلامية ولا نصرة ليلصقوا
    بهم منفردين تهمة الارهاب !! انتم ام واب واخ وابن وجد الارهاب ..  Not Daashaa of
    the pistol in his hand .. he''s a French soldier executions carried out against
    unarmed civilians in Algeria !!! If not then it does not regulate not base an
    Islamic state nor a victory for Alsqoa their individual terrorism charge !! You
    are a mother and father and brother and the son of terror found ..  Non Daashaa
    du pistolet dans sa main .. Il est un soldat français exécutions menées contre
    des civils non armés en Algérie !!! Si non, alors il ne réglemente pas pas fonder
    un Etat islamique, ni une victoire pour Alsqoa leur charge individuelle du terrorisme
    !! Vous êtes une mère et père et le frère et le fils de la terreur trouvé .. #
    occupant'
  sentences:
  - Massacre perpétré par des soldats français en Algérie
  - Video Of Attack On UP Minister Shrikant Sharma
  - Map shows there are no wildfires in Canada and Mexico
---

# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-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:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision ca1791e0bcc104f6db161f27de1340241b13c5a4 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 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': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
  (2): Normalize()
)
```

## 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 = [
    "ليس داعشياً من بيده المسدس ..انه جندي فرنسي ينفذ اعدامات بحق مواطنين عزل في الجزائر !!! لم يكن حينها لا تنظيم قاعدة ولا دولة اسلامية ولا نصرة ليلصقوا بهم منفردين تهمة الارهاب !! انتم ام واب واخ وابن وجد الارهاب ..  Not Daashaa of the pistol in his hand .. he's a French soldier executions carried out against unarmed civilians in Algeria !!! If not then it does not regulate not base an Islamic state nor a victory for Alsqoa their individual terrorism charge !! You are a mother and father and brother and the son of terror found ..  Non Daashaa du pistolet dans sa main .. Il est un soldat français exécutions menées contre des civils non armés en Algérie !!! Si non, alors il ne réglemente pas pas fonder un Etat islamique, ni une victoire pour Alsqoa leur charge individuelle du terrorisme !! Vous êtes une mère et père et le frère et le fils de la terreur trouvé .. # occupant",
    'Massacre perpétré par des soldats français en Algérie',
    'Video Of Attack On UP Minister Shrikant Sharma',
]
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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## 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: 140.38 tokens</li><li>max: 2514 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 20.49 tokens</li><li>max: 141 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>Olhem aí a mineradora da Noruega destruindo o meio ambiente na Amazônia. Lula vendeu o solo para a Noruega em documento secreto. Ela arrecada 2 bilhoes ao ano e devolve 180 milhoes para consertar o estrago que ela mesmo faz na Amazônia.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | <code>O ex-presidente Lula vendeu o solo da Amazônia para uma empresa norueguesa</code>                                         | <code>1.0</code> |
  | <code>EL CONGRESO DANIE Cometió una burrada Al aprobar en primera votación con 113 votos a favor, 5 en contra y una abstención, que la vacuna contra el coronavirus sea de manera OBLIGATORIA para todos Que les pasa a estos genios de la política, acaso no saben que están violando leyes universales de Derechos Humanos¿Qué les pasa a estos congresistas?. . ¿ Acaso desconocen y pisotean las leyes internacionales que respaldan los Derechos Humanos Universales ???. . Absolutamente nadie puede ser obligado a vacunarse. . Igualmente, ningún procedimiento médico puede hacerse sin el consentimiento del paciente. . No lo digo yo, lo dice la UNESCO,la Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura.... Que en sus normativas explican lo siguiente : . SOLO UNO MISMO TIENE EL CONTROL DE SU PROPIO CUERPO, nadie tiene el control de nuestro cuerpo más que uno mismo, nadie puede intervenir en nuestro cuerpo bajo ninguna circunstancia sin nuestro consentimiento. . Legalmente bajo t...</code> | <code>En Perú el Congreso aprobó que la vacuna contra el covid-19 sea obligatoria</code>                                        | <code>1.0</code> |
  | <code>Why changes to Legislation is so difficult. Debating PTSD in Emergency Services Debating Mental Health Stigma Debating Workers Compensation Debating Cancer Legislation for Firefighters Debating MP's Pay Debating PFAS Contamination Debating Suicide Figures in Australia Debating MP's AllowancesThis tells us everything we need to know about this Government’s priorities.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | <code>Accurate description of photos showing the difference in attendance in various parliamentary sessions in Australia</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`: 1
- `per_device_eval_batch_size`: 1
- `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`: 1
- `per_device_eval_batch_size`: 1
- `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 |
|:------:|:-----:|:-------------:|
| 0.0194 | 500   | 0.0           |
| 0.0388 | 1000  | 0.0           |
| 0.0583 | 1500  | 0.0           |
| 0.0777 | 2000  | 0.0           |
| 0.0971 | 2500  | 0.0           |
| 0.1165 | 3000  | 0.0           |
| 0.1360 | 3500  | 0.0           |
| 0.1554 | 4000  | 0.0           |
| 0.1748 | 4500  | 0.0           |
| 0.1942 | 5000  | 0.0           |
| 0.2137 | 5500  | 0.0           |
| 0.2331 | 6000  | 0.0           |
| 0.2525 | 6500  | 0.0           |
| 0.2719 | 7000  | 0.0           |
| 0.2913 | 7500  | 0.0           |
| 0.3108 | 8000  | 0.0           |
| 0.3302 | 8500  | 0.0           |
| 0.3496 | 9000  | 0.0           |
| 0.3690 | 9500  | 0.0           |
| 0.3885 | 10000 | 0.0           |
| 0.4079 | 10500 | 0.0           |
| 0.4273 | 11000 | 0.0           |
| 0.4467 | 11500 | 0.0           |
| 0.4661 | 12000 | 0.0           |
| 0.4856 | 12500 | 0.0           |
| 0.5050 | 13000 | 0.0           |
| 0.5244 | 13500 | 0.0           |
| 0.5438 | 14000 | 0.0           |
| 0.5633 | 14500 | 0.0           |
| 0.5827 | 15000 | 0.0           |
| 0.6021 | 15500 | 0.0           |
| 0.6215 | 16000 | 0.0           |
| 0.6410 | 16500 | 0.0           |
| 0.6604 | 17000 | 0.0           |
| 0.6798 | 17500 | 0.0           |
| 0.6992 | 18000 | 0.0           |
| 0.7186 | 18500 | 0.0           |
| 0.7381 | 19000 | 0.0           |
| 0.7575 | 19500 | 0.0           |
| 0.7769 | 20000 | 0.0           |
| 0.7963 | 20500 | 0.0           |
| 0.8158 | 21000 | 0.0           |
| 0.8352 | 21500 | 0.0           |
| 0.8546 | 22000 | 0.0           |
| 0.8740 | 22500 | 0.0           |
| 0.8934 | 23000 | 0.0           |
| 0.9129 | 23500 | 0.0           |
| 0.9323 | 24000 | 0.0           |
| 0.9517 | 24500 | 0.0           |
| 0.9711 | 25000 | 0.0           |
| 0.9906 | 25500 | 0.0           |


### 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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