sentence-transformers safetensors bilingual sentence-similarity feature-extraction generated_from_trainer dataset_size:21769 loss:MultipleNegativesRankingLoss custom_code arxiv:1908.10084 arxiv:1705.00652 base_model:am-azadi/bilingual-embedding-large_Fine_Tuned_1e base_model:finetune:am-azadi/bilingual-embedding-large_Fine_Tuned_1e autotrain_compatible endpoints_compatible region:us
---
base_model: am-azadi/bilingual-embedding-large_Fine_Tuned_1e
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:21769
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'GOOD NEWS!  Eriksen, has already gone out to the hospital window,
    where he is under observation and looks optimistic after having suffered a cardiac
    arrest. '
  sentences:
  - Bolsonaro with the two assassins of Marielle Franco No, the men next to Jair Bolsonaro
    in this photo are not the ones accused of the murder of Marielle Franco
  - This photo shows Christian Eriksen waving from the window of the hospital where
    he was admitted after suffering cardiac arrest The photo of Eriksen waving from
    the window was taken months before his heart incident
  - Video of protests in the US during the COVID-19 pandemic This video has been circulating
    in reports about the funeral procession of military commanders in Iran in January
    2020
- source_sentence: What a dirty game... "US postman arrested in canadian border with
    banknotes stolen in the trunk of the car". 91 Breaking911  5h U.S. Postal Worker
    Caught at Canadian Border With Stolen Ballots In Car Trunk - breaking911.com/u-s-postal-wor...
    8218248 Claudia Wild   IT 8206434 300   4:57 06 Nov 20 Twitter for iPhone 1,134
    Retweets 113 Tweets with comment
  sentences:
  - Postman arrested with stolen bills at US-Canada border Only three blank bills
    were found in a US postal worker's car
  - Covid relief plan will cost every American $5,750 Misleading posts claim US covid
    relief plan costs every American $5,750
  - CDC informs that 10% of the swabs used for PCR testing were sent to LABORATORIES,
    being analyzed of GENETIC SEQUENCES We check the claim that PCR tests aim to sequence
    the DNA of patients with covid-19
- source_sentence: '. Northeast Always in Our Hearts! Advance Northeast!!  . Brazilian
    Army through its Engineering Battalion finds a Huge Potable Water Well in Seridó
    - Caicó/RN, one of the most needy areas. This well will supply the homes of more
    than 3,000 people!!  . It''s our President Bolsonaro ridding the Bravo People
    of the Northeast from the wounds of drought! .  . .  BRAZIL LOVED HOMELAND  .
    . Friends and Followers of : Follow and Turn on our Notifications  . .          #
    pocket                  . '
  sentences:
  - Twitter suspended Elon Musk's Twitter account after he pulled out of deal Imposter
    Elon Musk Twitter account shared in false posts claiming he was 'suspended' over
    buyout row
  - The Brazilian Army found water in Caicó, Rio Grande do Norte, during the government
    of President Jair Bolsonaro. The recording of the drilling of an artesian well
    in Caicó, Rio Grande do Norte, has been circulating since 2015
  - A video was published today about Syrian refugees in Sweden being subjected to
    the separation of husbands, as well as the forcible removal of their children
    and the handing over of children to Christian families to change their religion.
    And to turn them into Christians, they will have two children Swedish police did
    not take Syrian children to hand over to Christian families
- source_sentence: what hp Álvaro Uribe Vélez ... 3pm ✓ The coastal people are the
    least intellectual of the country, that is why this region of Colombia is mired
    in poverty. They don't like to work either. that's why there is currently a level
    very high of misery in la guajira. With the democratic center we will change.
    The entire Caribbean coast must feel outraged by the statements of this individual.
    Now with more reasons, the coastal people should support Petro. The how.. see
    more
  sentences:
  - 'Covid-19: Omicron variant is transmitted by eye contact according to the WHO
    The coronavirus is transmitted by interaction with contaminated droplets, not
    by eye contact'
  - 5G causes suffocation in humans, affects the respiratory system There is no evidence
    that 5G technology affects the respiratory system and increases toxins in the
    body
  - Álvaro Uribe tweeted that the coastal people are the least intellectual population
    in Colombia There is no record of Uribe tweeting that the coast is the "least
    intellectual" region of Colombia
- source_sentence: 'The terrorists evaporated in seconds  A very rare scene of the
    moment the Egyptian planes bombed the terrorist elements in Sinai Watch the video
    here   NB Please all our followers on our page subscribe to our YouTube channel
    We will publish everything new on the ground Open the channel link '
  sentences:
  - Cars melt due to hot weather in Saudi Arabia No, these cars did not melt due to
    hot weather
  - Footage shows robbery in Sri Lanka Delhi crime footage falsely shared as 'Sri
    Lanka burglary'
  - A very rare scene of the moment the Egyptian planes bombed the terrorist elements
    in Sinai This picture is not of an Egyptian warplane, but of an Israeli plane
---

# SentenceTransformer based on am-azadi/bilingual-embedding-large_Fine_Tuned_1e

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [am-azadi/bilingual-embedding-large_Fine_Tuned_1e](https://huggingface.co/am-azadi/bilingual-embedding-large_Fine_Tuned_1e). 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/bilingual-embedding-large_Fine_Tuned_1e](https://huggingface.co/am-azadi/bilingual-embedding-large_Fine_Tuned_1e) <!-- at revision 9212ebc911617536aa06e4fe49c33d6f93ace38a -->
- **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: BilingualModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
  (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 = [
    'The terrorists evaporated in seconds  A very rare scene of the moment the Egyptian planes bombed the terrorist elements in Sinai Watch the video here   NB Please all our followers on our page subscribe to our YouTube channel We will publish everything new on the ground Open the channel link ',
    'A very rare scene of the moment the Egyptian planes bombed the terrorist elements in Sinai This picture is not of an Egyptian warplane, but of an Israeli plane',
    'Cars melt due to hot weather in Saudi Arabia No, these cars did not melt due to hot weather',
]
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: 21,769 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                          | sentence_1                                                                         |
  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                             |
  | details | <ul><li>min: 6 tokens</li><li>mean: 119.28 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 39.42 tokens</li><li>max: 98 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | sentence_1                                                                                                                                                             |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>HAPPENING NOW ; KENYA ELECTRIC BUS IS ON FIRE ALONG KAREN ROAD. </code>                                                                                                                                                                                                                                                                                                                                                                                                                                                             | <code>Electric bus catches fire in Nairobi Video shows a methane-powered bus that caught fire in Italy, not an electric bus in Kenya</code>                            |
  | <code> RUPTLY Viewed 51,670 times 8 hours Snorr On the way down Khao Pak Thong Chai, route 3-4, Sattahip - Korat, all of them would have died. pity Incident 27 Jun.</code>                                                                                                                                                                                                                                                                                                                                                               | <code>Video showing road accidents in Thailand? This is a video published in a news report about a car crash in Russia.</code>                                         |
  | <code>The image that went around the world! This photo won the best of the decade award and led to the author to depression, the author narrated in his description; "Cheetahs chased a mother deer and her 2 babies, she offered herself so that her children could escape and in the photo looks like she watches her babies run to safety as she is about to be devoured" How many times have you stopped to think how many sacrifices your parents do for you. While you have fun, laugh and you enjoy life, they give theirs.</code> | <code>Cheetahs chased a mother deer and she volunteered so her children could escape Behind the picture: Cheetahs learned from their mother how to capture prey</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 |
|:------:|:-----:|:-------------:|
| 0.0459 | 500   | 0.0135        |
| 0.0919 | 1000  | 0.024         |
| 0.1378 | 1500  | 0.0073        |
| 0.1837 | 2000  | 0.0103        |
| 0.2297 | 2500  | 0.0265        |
| 0.2756 | 3000  | 0.0209        |
| 0.3215 | 3500  | 0.0308        |
| 0.3675 | 4000  | 0.0301        |
| 0.4134 | 4500  | 0.0382        |
| 0.4593 | 5000  | 0.0164        |
| 0.5053 | 5500  | 0.0251        |
| 0.5512 | 6000  | 0.0141        |
| 0.5972 | 6500  | 0.0131        |
| 0.6431 | 7000  | 0.006         |
| 0.6890 | 7500  | 0.0261        |
| 0.7350 | 8000  | 0.0111        |
| 0.7809 | 8500  | 0.0089        |
| 0.8268 | 9000  | 0.0201        |
| 0.8728 | 9500  | 0.0175        |
| 0.9187 | 10000 | 0.0086        |
| 0.9646 | 10500 | 0.0049        |


### 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: 3.3.2
- 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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