README.md

---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
datasets:
- sentence-transformers/quora-duplicates
language:
- en
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:323432
- loss:OnlineContrastiveLoss
widget:
- source_sentence: How do I have a successful career in animation industry with all
    distance mode of education (from schooling)?
  sentences:
  - The LINE app is blocked in China. I bought a VPN, but it's still not working.
    Can someone help me?
  - What is independent?
  - How do I find all distance education schools in any city?
- source_sentence: How can I get the funding for my startup without revealing my idea?
  sentences:
  - How has demonetization affected big business people like Mukesh Ambani?
  - How should I go about getting funding for my idea?
  - What are the advantages and disadvantages of studying an MBBS in China?
- source_sentence: I am an okay looking young women but I am always feeling ugly since
    I'm not extremely beautiful. How can I stop those thoughts?
  sentences:
  - Whenever I think about my failures in life, I always feel that I lack some qualities.
    But which are those qualities, I am not able to find out. How can I find which
    qualities I lack?
  - What songs make you cry?
  - What does histrionic personality disorder feel like physically to you?
- source_sentence: What do you think of Prime Minister Narendra Modi's decision to
    introduce new INR 500 and INR 2000 currency notes?
  sentences:
  - What do you think of the decision by the Indian Government to replace 1000 notes
    with 2000 notes?
  - How do you find volume from density and mass?
  - What are the consequences of having a blood sugar level over 300?
- source_sentence: Why do complementary angles have to be adjacent?
  sentences:
  - What is an AEG airsoft gun?
  - How can I get rid of my bad habits?
  - Can two adjacent angles be complementary?
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy
      value: 0.8683618194860125
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7981455326080322
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.8292439905343131
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7598952651023865
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.7746589487768696
      name: Cosine Precision
    - type: cosine_recall
      value: 0.8921046460992195
      name: Cosine Recall
    - type: cosine_ap
      value: 0.8822291610822541
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.8359964382003018
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 17.112058639526367
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.7914425390403506
      name: Dot F1
    - type: dot_f1_threshold
      value: 16.083341598510742
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.7294350282485875
      name: Dot Precision
    - type: dot_recall
      value: 0.8649716946370549
      name: Dot Recall
    - type: dot_ap
      value: 0.8438654629805356
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.8568230725469341
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 46.94310760498047
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.8144082547946494
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 50.51482391357422
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.7656268427880646
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.8698288279234918
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.8636170591577621
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.8568849093472507
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 3.0017127990722656
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.8143016129285076
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 3.2429399490356445
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.7652309686542541
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.8700968076910194
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.8637642883474006
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.8683618194860125
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 46.94310760498047
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.8292439905343131
      name: Max F1
    - type: max_f1_threshold
      value: 50.51482391357422
      name: Max F1 Threshold
    - type: max_precision
      value: 0.7746589487768696
      name: Max Precision
    - type: max_recall
      value: 0.8921046460992195
      name: Max Recall
    - type: max_ap
      value: 0.8822291610822541
      name: Max Ap
---

# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) dataset. It maps sentences & paragraphs to a 384-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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
- **Language:** en
<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("DashReza7/paraphrase-multilingual-MiniLM-L12-v2_QuoraDuplicateDetection_FINETUNED")
# Run inference
sentences = [
    'Why do complementary angles have to be adjacent?',
    'Can two adjacent angles be complementary?',
    'How can I get rid of my bad habits?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

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## Evaluation

### Metrics

#### Binary Classification

* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.8684     |
| cosine_accuracy_threshold    | 0.7981     |
| cosine_f1                    | 0.8292     |
| cosine_f1_threshold          | 0.7599     |
| cosine_precision             | 0.7747     |
| cosine_recall                | 0.8921     |
| cosine_ap                    | 0.8822     |
| dot_accuracy                 | 0.836      |
| dot_accuracy_threshold       | 17.1121    |
| dot_f1                       | 0.7914     |
| dot_f1_threshold             | 16.0833    |
| dot_precision                | 0.7294     |
| dot_recall                   | 0.865      |
| dot_ap                       | 0.8439     |
| manhattan_accuracy           | 0.8568     |
| manhattan_accuracy_threshold | 46.9431    |
| manhattan_f1                 | 0.8144     |
| manhattan_f1_threshold       | 50.5148    |
| manhattan_precision          | 0.7656     |
| manhattan_recall             | 0.8698     |
| manhattan_ap                 | 0.8636     |
| euclidean_accuracy           | 0.8569     |
| euclidean_accuracy_threshold | 3.0017     |
| euclidean_f1                 | 0.8143     |
| euclidean_f1_threshold       | 3.2429     |
| euclidean_precision          | 0.7652     |
| euclidean_recall             | 0.8701     |
| euclidean_ap                 | 0.8638     |
| max_accuracy                 | 0.8684     |
| max_accuracy_threshold       | 46.9431    |
| max_f1                       | 0.8292     |
| max_f1_threshold             | 50.5148    |
| max_precision                | 0.7747     |
| max_recall                   | 0.8921     |
| **max_ap**                   | **0.8822** |

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## Training Details

### Training Dataset

#### sentence-transformers/quora-duplicates

* Dataset: [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 323,432 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                        | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                           | int                                             |
  | details | <ul><li>min: 6 tokens</li><li>mean: 16.39 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.2 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>0: ~62.10%</li><li>1: ~37.90%</li></ul> |
* Samples:
  | sentence1                                                                  | sentence2                                                              | label          |
  |:---------------------------------------------------------------------------|:-----------------------------------------------------------------------|:---------------|
  | <code>Which are the best compilers for C language (for Windows 10)?</code> | <code>Which is the best open source C/C++ compiler for Windows?</code> | <code>0</code> |
  | <code>How much does YouTube pay per 1000 views in India?</code>            | <code>How much does youtube pay per 1000 views?</code>                 | <code>0</code> |
  | <code>What parts do I need to build my own PC?</code>                      | <code>I want to build a new computer. What parts do I need?</code>     | <code>1</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

### Evaluation Dataset

#### sentence-transformers/quora-duplicates

* Dataset: [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 80,858 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                          | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                             | int                                             |
  | details | <ul><li>min: 6 tokens</li><li>mean: 16.48 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.76 tokens</li><li>max: 101 tokens</li></ul> | <ul><li>0: ~63.90%</li><li>1: ~36.10%</li></ul> |
* Samples:
  | sentence1                                                                | sentence2                                                                    | label          |
  |:-------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:---------------|
  | <code>How many stories got busted on Quora while being anonymous?</code> | <code>Can what I say on Quora anonymously be used against me legally?</code> | <code>0</code> |
  | <code>What are innovative mechanical component designs?</code>           | <code>What is the Innovation design?</code>                                  | <code>0</code> |
  | <code>What is the best way to learn phrasal verbs?</code>                | <code>Why should I learn phrasal verbs?</code>                               | <code>1</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: 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, '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`: 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_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | loss   | max_ap |
|:------:|:----:|:-------------:|:------:|:------:|
| 0.0791 | 100  | -             | 8.0607 | 0.8164 |
| 0.1582 | 200  | -             | 7.3012 | 0.8445 |
| 0.2373 | 300  | -             | 6.9626 | 0.8582 |
| 0.3165 | 400  | -             | 6.7901 | 0.8639 |
| 0.3956 | 500  | 7.5229        | 6.6498 | 0.8694 |
| 0.4747 | 600  | -             | 6.5315 | 0.8736 |
| 0.5538 | 700  | -             | 6.4686 | 0.8766 |
| 0.6329 | 800  | -             | 6.4027 | 0.8787 |
| 0.7120 | 900  | -             | 6.3108 | 0.8797 |
| 0.7911 | 1000 | 6.4636        | 6.2862 | 0.8812 |
| 0.8703 | 1100 | -             | 6.2449 | 0.8818 |
| 0.9494 | 1200 | -             | 6.2344 | 0.8822 |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.21.0
- 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",
}
```

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