Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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
+}

README.md

@@ -0,0 +1,463 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:28990
+- loss:CoSENTLoss
+base_model: jhu-clsp/mmBERT-small
+widget:
+- source_sentence: Bộ Y tế Trung Quốc cho biết thêm 5 người đã chết vì Sars và thêm
+    159 người bị nhiễm bệnh.
+  sentences:
+  - Hôm thứ Hai, Trung Quốc cho biết thêm 9 người đã chết vì SARS và 160 người nữa
+    bị nhiễm virus này.
+  - Cộng hòa Hồi giáo là nước áp dụng nhiều hình phạt tử hình nhất trên thế giới sau
+    Trung Quốc.
+  - Chỉ số Dow Jones Industrial Average tăng 194,14 điểm, hoặc 2,09%, lên 9.469,20
+    sau khi giảm hơn 1% một ngày trước đó.
+- source_sentence: Thừa kế là một khái niệm cơ bản trong lập trình hướng đối tượng.
+  sentences:
+  - Thừa kế trong lập trình hướng đối tượng là một cách để tạo các lớp mới bằng cách
+    sử dụng các lớp đã được định nghĩa.
+  - Một con chó màu nâu và trắng đang cầm một quả bóng chày trong miệng
+  - Tôi cũng đang gặp đúng vấn đề tương tự.
+- source_sentence: hành động bảo tồn hoặc bảo vệ một cái gì đó
+  sentences:
+  - 'Tôi sẽ nói thẳng ở đây: Bạn không.'
+  - Nhưng lợi nhuận khiến thị trường chứng khoán Mỹ trở nên hấp dẫn phản ánh sự cân
+    bằng chính trị mong manh.
+  - hành động khẳng định hoặc khẳng định hoặc tuyên bố một cái gì đó.
+- source_sentence: Một cậu bé mặc áo thun trắng đang phun nước trong nước nông
+  sentences:
+  - Một cậu bé đang chạy qua biển ở bãi biển
+  - Hai người đang đi xe đạp.
+  - Người đàn ông đang chơi với con chó nhỏ lông xù.
+- source_sentence: Một người đàn ông đang ngồi gần một chiếc xe đạp và đang viết một
+    ghi chú
+  sentences:
+  - 'Mức thuế 50 pence của Đảng Lao động: Những điều bạn cần biết.'
+  - Các vận động viên khuyết tật chuẩn bị sẵn sàng, làm dấy lên những câu hỏi về hậu
+    cần và sự công bằng.
+  - Một người đàn ông mặc quần áo được phủ sơn và đang ngồi bên ngoài trong một khu
+    vực đông đúc để viết một cái gì đó
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer based on jhu-clsp/mmBERT-small
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: 8Opt sts dev 0002
+      type: 8Opt-sts-dev-0002
+    metrics:
+    - type: pearson_cosine
+      value: 0.6930301743749289
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.7196305705541503
+      name: Spearman Cosine
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: 8Opt sts test 0002
+      type: 8Opt-sts-test-0002
+    metrics:
+    - type: pearson_cosine
+      value: 0.6929781201615477
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.7196122481384816
+      name: Spearman Cosine
+---
+
+# SentenceTransformer based on jhu-clsp/mmBERT-small
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jhu-clsp/mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small). 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:** [jhu-clsp/mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small) <!-- at revision abc32620dd4f6ab06f5fbe905dc25f310618e09f -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 384 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/huggingface/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': 8192, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
+  (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("8Opt/mmbert-small-vn-sts-001")
+# Run inference
+sentences = [
+    'Một người đàn ông đang ngồi gần một chiếc xe đạp và đang viết một ghi chú',
+    'Một người đàn ông mặc quần áo được phủ sơn và đang ngồi bên ngoài trong một khu vực đông đúc để viết một cái gì đó',
+    'Các vận động viên khuyết tật chuẩn bị sẵn sàng, làm dấy lên những câu hỏi về hậu cần và sự công bằng.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.9369, 0.9048],
+#         [0.9369, 1.0000, 0.8964],
+#         [0.9048, 0.8964, 1.0000]])
+```
+
+<!--
+### 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
+
+#### Semantic Similarity
+
+* Datasets: `8Opt-sts-dev-0002` and `8Opt-sts-test-0002`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | 8Opt-sts-dev-0002 | 8Opt-sts-test-0002 |
+|:--------------------|:------------------|:-------------------|
+| pearson_cosine      | 0.693             | 0.693              |
+| **spearman_cosine** | **0.7196**        | **0.7196**         |
+
+<!--
+## 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: 28,990 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                          |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 18.02 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.77 tokens</li><li>max: 81 tokens</li></ul> | <ul><li>min: 0.04</li><li>mean: 2.6</li><li>max: 5.0</li></ul> |
+* Samples:
+  | sentence1                                                   | sentence2                                                                      | score            |
+  |:------------------------------------------------------------|:-------------------------------------------------------------------------------|:-----------------|
+  | <code>Một con chó đen đang chạy trong tuyết.</code>         | <code>Một con chó đen đang chạy trên bãi biển.</code>                          | <code>1.8</code> |
+  | <code>bóng đèn a tạo ra một khoảng trống</code>             | <code>nếu bóng đèn a cháy ra, cả b và c đều không ở trong một đường kín</code> | <code>1.8</code> |
+  | <code>Sự phát triển an ninh tại Iraq, ngày 1 tháng 2</code> | <code>Sự phát triển an ninh tại Pakistan, ngày 13 tháng 3</code>               | <code>1.6</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 4,141 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                          | sentence2                                                                         | score                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | float                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 18.71 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 17.54 tokens</li><li>max: 91 tokens</li></ul> | <ul><li>min: 0.04</li><li>mean: 2.56</li><li>max: 5.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                                                   | sentence2                                                                                                                         | score            |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>một đơn vị đáp ứng các điều kiện tiên quyết để tham gia vào một sự kiện. một mức độ sửa đổi có thể được bao gồm để chỉ ra bao nhiêu đơn vị vượt quá hoặc không đạt được các yêu cầu tối thiểu.</code> | <code>(thường theo sau là `to ') có phương tiện cần thiết hoặc kỹ năng hoặc bí quyết hoặc thẩm quyền để làm một cái gì đó;</code> | <code>0.4</code> |
+  | <code>Tôi sẽ không đưa nó vào hồ sơ của mình.</code>                                                                                                                                                        | <code>Tôi sẽ không đưa công việc này vào hồ sơ của mình.</code>                                                                   | <code>4.0</code> |
+  | <code>Một cậu bé trẻ với một chiếc áo khoác chứa tim đang nâng tay lên khi anh ta trượt</code>                                                                                                              | <code>Một đứa trẻ tóc vàng đang đi xuống một slide và ném lên tay của mình</code>                                                 | <code>3.7</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 45
+- `per_device_eval_batch_size`: 45
+- `learning_rate`: 2e-05
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+
+#### 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`: 45
+- `per_device_eval_batch_size`: 45
+- `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`: 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`: 3
+- `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
+- `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}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `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
+- `hub_revision`: None
+- `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
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: no
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | 8Opt-sts-dev-0002_spearman_cosine | 8Opt-sts-test-0002_spearman_cosine |
+|:------:|:----:|:-------------:|:---------------:|:---------------------------------:|:----------------------------------:|
+| 0.1550 | 100  | 6.8187        | 6.7929          | 0.5004                            | -                                  |
+| 0.3101 | 200  | 6.7419        | 6.7168          | 0.6143                            | -                                  |
+| 0.4651 | 300  | 6.6975        | 6.7319          | 0.6434                            | -                                  |
+| 0.6202 | 400  | 6.6737        | 6.6706          | 0.6739                            | -                                  |
+| 0.7752 | 500  | 6.6784        | 6.7054          | 0.6772                            | -                                  |
+| 0.9302 | 600  | 6.6712        | 6.6551          | 0.6898                            | -                                  |
+| 1.0853 | 700  | 6.5671        | 6.6602          | 0.6959                            | -                                  |
+| 1.2403 | 800  | 6.5571        | 6.6496          | 0.6945                            | -                                  |
+| 1.3953 | 900  | 6.5368        | 6.6535          | 0.7046                            | -                                  |
+| 1.5504 | 1000 | 6.5372        | 6.6903          | 0.7055                            | -                                  |
+| 1.7054 | 1100 | 6.5123        | 6.6377          | 0.7162                            | -                                  |
+| 1.8605 | 1200 | 6.5288        | 6.6298          | 0.7181                            | -                                  |
+| 2.0155 | 1300 | 6.4976        | 6.6644          | 0.7204                            | -                                  |
+| 2.1705 | 1400 | 6.3824        | 6.7222          | 0.7172                            | -                                  |
+| 2.3256 | 1500 | 6.3512        | 6.7714          | 0.7165                            | -                                  |
+| 2.4806 | 1600 | 6.3392        | 6.7757          | 0.7161                            | -                                  |
+| 2.6357 | 1700 | 6.3487        | 6.7514          | 0.7205                            | -                                  |
+| 2.7907 | 1800 | 6.3476        | 6.7294          | 0.7195                            | -                                  |
+| 2.9457 | 1900 | 6.3519        | 6.7533          | 0.7196                            | -                                  |
+| -1     | -1   | -             | -               | -                                 | 0.7196                             |
+
+
+### Framework Versions
+- Python: 3.12.12
+- Sentence Transformers: 5.1.2
+- Transformers: 4.57.1
+- PyTorch: 2.8.0+cu126
+- Accelerate: 1.11.0
+- Datasets: 4.0.0
+- Tokenizers: 0.22.1
+
+## 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",
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@article{10531646,
+    author={Huang, Xiang and Peng, Hao and Zou, Dongcheng and Liu, Zhiwei and Li, Jianxin and Liu, Kay and Wu, Jia and Su, Jianlin and Yu, Philip S.},
+    journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
+    title={CoSENT: Consistent Sentence Embedding via Similarity Ranking},
+    year={2024},
+    doi={10.1109/TASLP.2024.3402087}
+}
+```
+
+<!--
+## 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.*
+-->

config.json

@@ -0,0 +1,46 @@
+{
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 1,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": 1,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000,
+  "gradient_checkpointing": false,
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