Add new SentenceTransformer model

1_Pooling/config.json

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+{
+  "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
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:63
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: keepitreal/vietnamese-sbert
+widget:
+- source_sentence: Samsung Galaxy S22 Ultra
+  sentences:
+  - Điện thoại camera 108MP
+  - Điện thoại RAM 12GB
+  - Điện thoại có zoom quang học 10x
+- source_sentence: Google Pixel 8 Pro
+  sentences:
+  - Điện thoại có jack cắm tai nghe 3.5mm
+  - Điện thoại có bộ nhớ trong 256GB
+  - Điện thoại chụp ảnh đẹp
+- source_sentence: Google Pixel 8
+  sentences:
+  - Điện thoại màn hình 120Hz
+  - Điện thoại giá rẻ
+  - Điện thoại Android mới nhất
+- source_sentence: JBL Reflect Flow Pro
+  sentences:
+  - iPhone mới nhất
+  - Điện thoại màn hình cong
+  - Điện thoại có loa Harman Kardon
+- source_sentence: Asus ROG Phone 7
+  sentences:
+  - Điện thoại có bút
+  - Điện thoại chơi game
+  - Điện thoại có đèn flash kép
+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: 0.2857142857142857
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.2857142857142857
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5714285714285714
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.0
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.09523809523809523
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.05714285714285715
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.05714285714285715
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.0
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.2857142857142857
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.2857142857142857
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5714285714285714
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.25679948860544627
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.1598639455782313
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.17696777071484332
+      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: 0.42857142857142855
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.5714285714285714
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7142857142857143
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.0
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.14285714285714285
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1142857142857143
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07142857142857142
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.0
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.42857142857142855
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.5714285714285714
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7142857142857143
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.3358736991627618
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.21564625850340136
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.22075481533609612
+      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: 0.2857142857142857
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5714285714285714
+      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: 0.05714285714285715
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.05714285714285715
+      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: 0.2857142857142857
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5714285714285714
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.22155623379830594
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.11564625850340135
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.13073998125841443
+      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: 0.14285714285714285
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.2857142857142857
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.42857142857142855
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.0
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.047619047619047616
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.05714285714285715
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.042857142857142864
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.0
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.14285714285714285
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.2857142857142857
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.42857142857142855
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.18057284162953233
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.10374149659863945
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.11943368484517551
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.14285714285714285
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.2857142857142857
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.2857142857142857
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5714285714285714
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.14285714285714285
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.09523809523809523
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.05714285714285715
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.05714285714285715
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.14285714285714285
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.2857142857142857
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.2857142857142857
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5714285714285714
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.32106066086016677
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.24801587301587302
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.2591176744402551
+      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/phone-search-model")
+# Run inference
+sentences = [
+    'Asus ROG Phone 7',
+    'Điện thoại chơi game',
+    'Điện thoại có đèn flash kép',
+]
+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]
+```
+
+<!--
+### 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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | dim_768    | dim_512    | dim_256    | dim_128    | dim_64     |
+|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1   | 0.0        | 0.0        | 0.0        | 0.0        | 0.1429     |
+| cosine_accuracy@3   | 0.2857     | 0.4286     | 0.0        | 0.1429     | 0.2857     |
+| cosine_accuracy@5   | 0.2857     | 0.5714     | 0.2857     | 0.2857     | 0.2857     |
+| cosine_accuracy@10  | 0.5714     | 0.7143     | 0.5714     | 0.4286     | 0.5714     |
+| cosine_precision@1  | 0.0        | 0.0        | 0.0        | 0.0        | 0.1429     |
+| cosine_precision@3  | 0.0952     | 0.1429     | 0.0        | 0.0476     | 0.0952     |
+| cosine_precision@5  | 0.0571     | 0.1143     | 0.0571     | 0.0571     | 0.0571     |
+| cosine_precision@10 | 0.0571     | 0.0714     | 0.0571     | 0.0429     | 0.0571     |
+| cosine_recall@1     | 0.0        | 0.0        | 0.0        | 0.0        | 0.1429     |
+| cosine_recall@3     | 0.2857     | 0.4286     | 0.0        | 0.1429     | 0.2857     |
+| cosine_recall@5     | 0.2857     | 0.5714     | 0.2857     | 0.2857     | 0.2857     |
+| cosine_recall@10    | 0.5714     | 0.7143     | 0.5714     | 0.4286     | 0.5714     |
+| **cosine_ndcg@10**  | **0.2568** | **0.3359** | **0.2216** | **0.1806** | **0.3211** |
+| cosine_mrr@10       | 0.1599     | 0.2156     | 0.1156     | 0.1037     | 0.248      |
+| cosine_map@100      | 0.177      | 0.2208     | 0.1307     | 0.1194     | 0.2591     |
+
+<!--
+## 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
+
+#### json
+
+* Dataset: json
+* Size: 63 training samples
+* Columns: <code>positive</code> and <code>anchor</code>
+* Approximate statistics based on the first 63 samples:
+  |         | positive                                                                        | anchor                                                                           |
+  |:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                          | string                                                                           |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 6.9 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 7.86 tokens</li><li>max: 12 tokens</li></ul> |
+* Samples:
+  | positive                                                   | anchor                                   |
+  |:-----------------------------------------------------------|:-----------------------------------------|
+  | <code>Google Pixel 8</code>                                | <code>Điện thoại Android mới nhất</code> |
+  | <code>Samsung Galaxy S22 Ultra</code>                      | <code>Điện thoại có sạc không dây</code> |
+  | <code>Samsung Galaxy Note 20 Ultra đi kèm bút S Pen</code> | <code>Điện thoại có bút</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** | **0.3188**             | **0.3971**             | **0.3073**             | **0.1945**             | **0.2442**            |
+| 2.0     | 2     | 0.3209                 | 0.3886                 | 0.2545                 | 0.1838                 | 0.3194                |
+| 3.0     | 3     | 0.2542                 | 0.3359                 | 0.2391                 | 0.1838                 | 0.3211                |
+| 4.0     | 4     | 0.2568                 | 0.3359                 | 0.2216                 | 0.1806                 | 0.3211                |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.11.0
+- Sentence Transformers: 3.3.1
+- Transformers: 4.41.2
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.1.1
+- 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}
+}
+```
+
+<!--
+## 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.*
+-->

added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "<mask>": 64000
+}

config.json

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "keepitreal/vietnamese-sbert",
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 258,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "PhobertTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 64001
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e173812ff9d883f3b25efe8a56d63606f450e97c4307c7b56bb36ea0d593b65c
+size 540015464

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,9 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

@@ -0,0 +1,54 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "64000": {
+      "content": "<mask>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 256,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "PhobertTokenizer",
+  "unk_token": "<unk>"
+}