README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:333
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: keepitreal/vietnamese-sbert
widget:
- source_sentence: Tôi Thấy Hoa Vàng Trên Cỏ Xanh
  sentences:
  - mềm mại, thoáng khí và bền đẹp
  - Nike Air Force 1 phong cách không lỗi mốt
  - Tôi Thấy Hoa Vàng Trên Cỏ Xanh thông điệp trân trọng tuổi thơ và cuộc sống bình
    dị
- source_sentence: iPhone 16
  sentences:
  - Cà Phê Cùng Tony kết hợp giải trí và giáo dục
  - iPhone 16 Pro RAM 12GB đa nhiệm mạnh mẽ
  - Loafer Gucci size từ 38 đến 45
- source_sentence: Áo Thun
  sentences:
  - phù hợp trong thời tiết nóng bức
  - thấm hút mồ hôi, nhẹ và thoáng khí
  - Giày chạy đường dài bền nhẹ
- source_sentence: Son Môi MAC Matte Lipstick - Ruby Woo
  sentences:
  - bảo quản dễ dàng bằng cách lộn trái khi giặt, tránh chất tẩy mạnh và phơi nơi
    thoáng mát
  - chất son lì mịn, bám màu 6-8 giờ
  - tác phẩm kinh điển về tâm linh và triết học
- source_sentence: LEGO City Police Station
  sentences:
  - mô hình đẹp mắt để trưng bày
  - dễ dàng phối đồ từ áo thun, sơ mi đến blazer
  - chỉ số SPF 50+ PA+++ bảo vệ tối ưu khỏi tia UV
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on keepitreal/vietnamese-sbert
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.0
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.0
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.02702702702702703
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5675675675675675
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.005405405405405406
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.056756756756756774
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.0
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.02702702702702703
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5675675675675675
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.1783581729179075
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.07062419562419564
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.07973358512714
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.0
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.0
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5405405405405406
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.0
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.054054054054054064
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.0
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5405405405405406
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.1701742309301506
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.06747104247104248
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.0782135520060237
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.0
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.0
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5405405405405406
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.0
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.054054054054054064
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.0
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5405405405405406
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.17224374024595593
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.06948734448734449
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.07938312163919391
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.0
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.0
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5405405405405406
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.0
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.054054054054054064
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.0
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5405405405405406
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.1706353981690823
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.06785714285714285
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.07606072355570134
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.0
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.0
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.02702702702702703
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5135135135135135
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.005405405405405406
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05135135135135136
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.0
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.02702702702702703
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5135135135135135
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.16481648451068456
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.06733161733161734
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.07793528025726168
      name: Cosine Map@100
---

# SentenceTransformer based on keepitreal/vietnamese-sbert

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [keepitreal/vietnamese-sbert](https://huggingface.co/keepitreal/vietnamese-sbert) on the json dataset. 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:** [keepitreal/vietnamese-sbert](https://huggingface.co/keepitreal/vietnamese-sbert) <!-- at revision a9467ef2ef47caa6448edeabfd8e5e5ce0fa2a23 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
<!-- - **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': 256, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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("NghiBuine/ecommerce-search-model")
# Run inference
sentences = [
    'LEGO City Police Station',
    'mô hình đẹp mắt để trưng bày',
    'dễ dàng phối đồ từ áo thun, sơ mi đến blazer',
]
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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### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
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### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation

### Metrics

#### Information Retrieval

* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
  ```json
  {
      "truncate_dim": 768
  }
  ```

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.0        |
| cosine_accuracy@3   | 0.0        |
| cosine_accuracy@5   | 0.027      |
| cosine_accuracy@10  | 0.5676     |
| cosine_precision@1  | 0.0        |
| cosine_precision@3  | 0.0        |
| cosine_precision@5  | 0.0054     |
| cosine_precision@10 | 0.0568     |
| cosine_recall@1     | 0.0        |
| cosine_recall@3     | 0.0        |
| cosine_recall@5     | 0.027      |
| cosine_recall@10    | 0.5676     |
| **cosine_ndcg@10**  | **0.1784** |
| cosine_mrr@10       | 0.0706     |
| cosine_map@100      | 0.0797     |

#### Information Retrieval

* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
  ```json
  {
      "truncate_dim": 512
  }
  ```

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.0        |
| cosine_accuracy@3   | 0.0        |
| cosine_accuracy@5   | 0.0        |
| cosine_accuracy@10  | 0.5405     |
| cosine_precision@1  | 0.0        |
| cosine_precision@3  | 0.0        |
| cosine_precision@5  | 0.0        |
| cosine_precision@10 | 0.0541     |
| cosine_recall@1     | 0.0        |
| cosine_recall@3     | 0.0        |
| cosine_recall@5     | 0.0        |
| cosine_recall@10    | 0.5405     |
| **cosine_ndcg@10**  | **0.1702** |
| cosine_mrr@10       | 0.0675     |
| cosine_map@100      | 0.0782     |

#### Information Retrieval

* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
  ```json
  {
      "truncate_dim": 256
  }
  ```

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.0        |
| cosine_accuracy@3   | 0.0        |
| cosine_accuracy@5   | 0.0        |
| cosine_accuracy@10  | 0.5405     |
| cosine_precision@1  | 0.0        |
| cosine_precision@3  | 0.0        |
| cosine_precision@5  | 0.0        |
| cosine_precision@10 | 0.0541     |
| cosine_recall@1     | 0.0        |
| cosine_recall@3     | 0.0        |
| cosine_recall@5     | 0.0        |
| cosine_recall@10    | 0.5405     |
| **cosine_ndcg@10**  | **0.1722** |
| cosine_mrr@10       | 0.0695     |
| cosine_map@100      | 0.0794     |

#### Information Retrieval

* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
  ```json
  {
      "truncate_dim": 128
  }
  ```

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.0        |
| cosine_accuracy@3   | 0.0        |
| cosine_accuracy@5   | 0.0        |
| cosine_accuracy@10  | 0.5405     |
| cosine_precision@1  | 0.0        |
| cosine_precision@3  | 0.0        |
| cosine_precision@5  | 0.0        |
| cosine_precision@10 | 0.0541     |
| cosine_recall@1     | 0.0        |
| cosine_recall@3     | 0.0        |
| cosine_recall@5     | 0.0        |
| cosine_recall@10    | 0.5405     |
| **cosine_ndcg@10**  | **0.1706** |
| cosine_mrr@10       | 0.0679     |
| cosine_map@100      | 0.0761     |

#### Information Retrieval

* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
  ```json
  {
      "truncate_dim": 64
  }
  ```

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.0        |
| cosine_accuracy@3   | 0.0        |
| cosine_accuracy@5   | 0.027      |
| cosine_accuracy@10  | 0.5135     |
| cosine_precision@1  | 0.0        |
| cosine_precision@3  | 0.0        |
| cosine_precision@5  | 0.0054     |
| cosine_precision@10 | 0.0514     |
| cosine_recall@1     | 0.0        |
| cosine_recall@3     | 0.0        |
| cosine_recall@5     | 0.027      |
| cosine_recall@10    | 0.5135     |
| **cosine_ndcg@10**  | **0.1648** |
| cosine_mrr@10       | 0.0673     |
| cosine_map@100      | 0.0779     |

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## 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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 333 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 333 samples:
  |         | positive                                                                         | anchor                                                                            |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 9.73 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.71 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
  | positive                                    | anchor                                                                             |
  |:--------------------------------------------|:-----------------------------------------------------------------------------------|
  | <code>Giày Chạy Bộ Adidas Ultraboost</code> | <code>Ultraboost đế continental chống trượt</code>                                 |
  | <code>Cà Phê Cùng Tony</code>               | <code>Cà Phê Cùng Tony chia sẻ bài học phát triển bản thân và sống tích cực</code> |
  | <code>Đắc Nhân Tâm</code>                   | <code>phát triển kỹ năng thuyết phục và giao tiếp tự nhiên</code>                  |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `bf16`: True
- `load_best_model_at_end`: True

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `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`: 4
- `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`: True
- `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`: True
- `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
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step  | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 1.0     | 1     | 0.1716                 | 0.1897                 | 0.1450                 | 0.1699                 | 0.1542                |
| **2.0** | **3** | **0.179**              | **0.171**              | **0.1722**             | **0.1719**             | **0.1644**            |
| 2.9091  | 4     | 0.1784                 | 0.1702                 | 0.1722                 | 0.1706                 | 0.1648                |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 4.1.0
- Transformers: 4.41.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 2.19.1
- 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",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### 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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