sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:25743 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:am-azadi/UAE-Large-V1_Fine_Tuned base_model:finetune:am-azadi/UAE-Large-V1_Fine_Tuned autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: am-azadi/UAE-Large-V1_Fine_Tuned
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: 'The good news: That was it with the vaccination terror Every compulsory
    vaccination is now illegal from the outset. The Council of Europe (not to be confused
    with the EU), to which all European states belong with the exception of Belarus,
    Kosovo and the Vatican and which is the body responsible for the European Court
    of Human Rights, passed a resolution on January 27th, 2021 in its resolution 2361/2021
    that no one may be vaccinated against their will, under pressure. The 47 member
    states are asked to point out before vaccination that vaccination is not compulsory
    and that the non-vaccinated person must not be discriminated against. Discrimination
    is expressly prohibited even if there are health risks or if someone does not
    want to be vaccinated. Vaccine manufacturers are encouraged to publish all information
    on the safety of the vaccines. With this resolution, the most important human
    rights organization in Europe has now set standards and obligations, as well as
    created guidelines under international law that are to be applied by the 47 member
    states, including the EU as an organization. Discrimination in the workplace,
    for example, or a ban on travel for the unvaccinated are thus legally excluded.
    You can now invoke it in every court case, before every authority, every employer,
    every tour operator, every home manager, etc.'
  sentences:
  - Mike Tyson prays in coffee shop which has “dogs and Muslims are not allowed” sign
  - The Council of Europe has declared mandatory corona vaccination illegal
  - CDC chief admits most Covid deaths were people with comorbidities
- source_sentence: ELPunctual The Punctual 24H  ⠀  More Madrid intends close the Pandemic
    Hospital Isabel Zendal if she wins the elections. 18:30 Mar 22 21 Twitter WebAppFirst
    they complain that there were no beds and now they are willing to remove them...
    Who votes for these crazy people?
  sentences:
  - Video shows Bolsonarista demonstration held in Rio de Janeiro on May 1, 2022
  - US missionary woman undergoes FGM in Kenya
  - More Madrid intends to close the Isabel Zendal Hospital if it wins the elections
- source_sentence: LAST DAM BUILT IN AUSTRALIA 1984 POPULATION OF AUSTRALIA 1984 15.5
    MILLION POPULATION OF AUSTRALIA 2018 25 MILLION AND WE WONDER WHY WE HAVE NO WATERThink
    about it...
  sentences:
  - The last dam in Australia was built in 1984
  - Demonstrators storm the White House
  - These ants contain the coronavirus in their body
- source_sentence: Quaranta QUARANTINE NCR TV ENHANCED COMMUNITY QUARANTINE EXTENDED
    Until May 30, 2020 BULACAN -NEW ECIJA TIMPAMPANGA TARLAC - ZAMBALES CALABARZON
    BENGUET PANGASINAN BATAAN AUNTIES ILOILO CEBU CEBU CITY - COME ON RAN FROM THE
    CLOUD DAVAO CI ORIENTAL MIND ALBAY CATANDUANE AntiqueECQ extended until may 30
    2020
  sentences:
  - Genuine news report about extension of COVID-19 lockdown in the Philippines
  - China built 1,000 beds in 10 days. Brazilian army builds 2,000 in 48 hours.
  - Holding your breath for 10 seconds allows you to identify contamination by the
    new coronavirus
- source_sentence: 'Look what a show Pope Francis gave in yesterday''s homily / sermon!
    It''s to be read and reread over and over again... This is the most spiritual
    Pope since Peter. "You may have flaws, be anxious, and sometimes live irritated,
    but don''t forget that your life is the greatest company in the world. Only you
    can prevent it from going into decline. Many appreciate you, admire you and love
    you. remember that being happy is not having a sky without storms, a road without
    accidents, work without fatigue, relationships without disappointments. Being
    happy is finding strength in forgiveness, hope in battles, security in the stage
    of fear, love in discord. It''s not just appreciating the smile, but also reflecting
    on sadness. It''s not just celebrating successes, but learning lessons from failures.
    It''s not just feeling happy with applause, but being happy in anonymity. Being
    happy is recognizing that it''s worth life is worth living, despite all the challenges,
    misunderstandings, periods of crisis. Being happy is not a fatality of fate, but
    an achievement for those who manage to travel within themselves. To be happy is
    to stop feeling like a victim of problems and become the author of his own story
    . It''s crossing deserts outside of yourself, but managing to find an oasis in
    the depths of our soul. It is to thank God for each morning, for the miracle of
    life. Being happy is not being afraid of your own feelings. It''s knowing how
    to talk about yourself. It''s having the courage to hear a "no". It''s feeling
    safe when receiving criticism, even if unfair. It''s kissing the children, pampering
    the parents, living poetic moments with friends, even when they hurt us. To be
    happy is to let the creature that lives in each of us live, free, joyful and simple.
    It''s having maturity to be able to say: "I was wrong". It''s having the courage
    to say, "I''m sorry". It''s having the sensitivity to say: "I need you". It''s
    having the ability to say, "I love you". May your life become a garden of opportunities
    to be happy... May your springtime be a lover of joy. May in your winters be a
    lover of wisdom. And that when you make a mistake, start over from the beginning.
    For only then will you be in love with life. You will discover that being happy
    is not having a perfect life. But using tears to irrigate tolerance. Use losses
    to train patience. Using mistakes to sculpt serenity. Using pain to cut pleasure.
    Use obstacles to open intelligence windows. Never give up....Never give up on
    the people who love you. Never give up happiness, for life is an incredible spectacle."
    (Pope Francis).'
  sentences:
  - '"The message that Pope Francis gave in yesterday''s homily/sermon! It is to be
    read and reread several times... What an admirable man!"'
  - Denmark allows Muslim women to wear the niqab
  - Trump author of speech disparaging Africans and Arabs
---

# SentenceTransformer based on am-azadi/UAE-Large-V1_Fine_Tuned

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [am-azadi/UAE-Large-V1_Fine_Tuned](https://huggingface.co/am-azadi/UAE-Large-V1_Fine_Tuned). 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:** [am-azadi/UAE-Large-V1_Fine_Tuned](https://huggingface.co/am-azadi/UAE-Large-V1_Fine_Tuned) <!-- at revision 8ea63aea71614563429bfd78c11b294cb6c1b3e5 -->
- **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 = [
    'Look what a show Pope Francis gave in yesterday\'s homily / sermon! It\'s to be read and reread over and over again... This is the most spiritual Pope since Peter. "You may have flaws, be anxious, and sometimes live irritated, but don\'t forget that your life is the greatest company in the world. Only you can prevent it from going into decline. Many appreciate you, admire you and love you. remember that being happy is not having a sky without storms, a road without accidents, work without fatigue, relationships without disappointments. Being happy is finding strength in forgiveness, hope in battles, security in the stage of fear, love in discord. It\'s not just appreciating the smile, but also reflecting on sadness. It\'s not just celebrating successes, but learning lessons from failures. It\'s not just feeling happy with applause, but being happy in anonymity. Being happy is recognizing that it\'s worth life is worth living, despite all the challenges, misunderstandings, periods of crisis. Being happy is not a fatality of fate, but an achievement for those who manage to travel within themselves. To be happy is to stop feeling like a victim of problems and become the author of his own story . It\'s crossing deserts outside of yourself, but managing to find an oasis in the depths of our soul. It is to thank God for each morning, for the miracle of life. Being happy is not being afraid of your own feelings. It\'s knowing how to talk about yourself. It\'s having the courage to hear a "no". It\'s feeling safe when receiving criticism, even if unfair. It\'s kissing the children, pampering the parents, living poetic moments with friends, even when they hurt us. To be happy is to let the creature that lives in each of us live, free, joyful and simple. It\'s having maturity to be able to say: "I was wrong". It\'s having the courage to say, "I\'m sorry". It\'s having the sensitivity to say: "I need you". It\'s having the ability to say, "I love you". May your life become a garden of opportunities to be happy... May your springtime be a lover of joy. May in your winters be a lover of wisdom. And that when you make a mistake, start over from the beginning. For only then will you be in love with life. You will discover that being happy is not having a perfect life. But using tears to irrigate tolerance. Use losses to train patience. Using mistakes to sculpt serenity. Using pain to cut pleasure. Use obstacles to open intelligence windows. Never give up....Never give up on the people who love you. Never give up happiness, for life is an incredible spectacle." (Pope Francis).',
    '"The message that Pope Francis gave in yesterday\'s homily/sermon! It is to be read and reread several times... What an admirable man!"',
    'Denmark allows Muslim women to wear the niqab',
]
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]
```

<!--
### 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: 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: 100.7 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 17.62 tokens</li><li>max: 179 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>best music k.m KOSE CELLIE HINS GUINOT SKIN CARE KWhat people fear most is not being physically disabled, but giving up on themselves. There are still many beautiful things in life to aspire to! This stunning performance, known as the American spirit, brought tears to the eyes of 10,000 spectators. Male dancer Babo has been blind since childhood due to a fire in his home. In order to protect him, his mother held him tightly in her arms and jumped from the 7th floor. The mother died as a result, and the little baby became blind due to bleeding from the fundus. His mother was an ice skater before he died, and Babo also had a soft spot for ice skating. Although he couldn't see anything, he still pursued dance enthusiastically. He danced the famous tango "La Cumparsita" with his partner at the World Figure Skating Championships in Helsinki! 1. His ears are like bats that can measure the sound and distance around him. 2. The female dancer is very amazing. She danced with him and led him for...</code> | <code>Performance by a blind American ice dancer</code>                                                                   | <code>1.0</code> |
  | <code>Photo from 2016. "Good" times when health was "fine" and the press did not report anything about. Bunch of Hypocrites...Let's go fight my people... .      left  right  not           army    above all</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | <code>Photo of a hospital in 2016. Good times when health was "good" and the press didn't report anything about it</code> | <code>1.0</code> |
  | <code>Haifa Oh Tel Aviv-Yafo Oh N WEST BANK Jerusalem is GAZA STRIPE Be'er Sheva Israel 65 65 35 35 15 M5 10 40Google and Apple maps have officially removed Palestine from the World Maps. Today Palestine was erased from the maps tomorrow Palestine will be erased from the world. PUT PALESTINE BACK ON THE MAP.  Please unite now Pakistanio. Enemy is very strong if we are divided. Think just about Pakistan. Support each other, support Pakistan and support your leadership.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | <code>Google and Apple removed Palestine from its maps</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   | 0.0173        |
| 0.0777 | 1000  | 0.0124        |
| 0.1165 | 1500  | 0.0127        |
| 0.1554 | 2000  | 0.0256        |
| 0.1942 | 2500  | 0.0123        |
| 0.2331 | 3000  | 0.0199        |
| 0.2719 | 3500  | 0.0079        |
| 0.3108 | 4000  | 0.0134        |
| 0.3496 | 4500  | 0.0127        |
| 0.3884 | 5000  | 0.026         |
| 0.4273 | 5500  | 0.0314        |
| 0.4661 | 6000  | 0.0267        |
| 0.5050 | 6500  | 0.0145        |
| 0.5438 | 7000  | 0.0093        |
| 0.5827 | 7500  | 0.007         |
| 0.6215 | 8000  | 0.0071        |
| 0.6603 | 8500  | 0.0116        |
| 0.6992 | 9000  | 0.0085        |
| 0.7380 | 9500  | 0.0157        |
| 0.7769 | 10000 | 0.0051        |
| 0.8157 | 10500 | 0.0101        |
| 0.8546 | 11000 | 0.0174        |
| 0.8934 | 11500 | 0.0116        |
| 0.9323 | 12000 | 0.0073        |
| 0.9711 | 12500 | 0.0146        |
| 0.0388 | 500   | 0.0115        |
| 0.0777 | 1000  | 0.0083        |
| 0.1165 | 1500  | 0.0287        |
| 0.1554 | 2000  | 0.0086        |
| 0.1942 | 2500  | 0.0157        |
| 0.2331 | 3000  | 0.0082        |
| 0.2719 | 3500  | 0.0116        |
| 0.3108 | 4000  | 0.0044        |
| 0.3496 | 4500  | 0.0158        |
| 0.3884 | 5000  | 0.0094        |
| 0.4273 | 5500  | 0.0087        |
| 0.4661 | 6000  | 0.0045        |
| 0.5050 | 6500  | 0.0139        |
| 0.5438 | 7000  | 0.0125        |
| 0.5827 | 7500  | 0.0196        |
| 0.6215 | 8000  | 0.0054        |
| 0.6603 | 8500  | 0.0061        |
| 0.6992 | 9000  | 0.0058        |
| 0.7380 | 9500  | 0.0243        |
| 0.7769 | 10000 | 0.0022        |
| 0.8157 | 10500 | 0.0083        |
| 0.8546 | 11000 | 0.0026        |
| 0.8934 | 11500 | 0.0036        |
| 0.9323 | 12000 | 0.0069        |
| 0.9711 | 12500 | 0.0071        |


### 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}
}
```

<!--
## 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.*
-->