Tay–Vietnamese embedding trained with contrastive learning

.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": 1024,
+    "pooling_mode_cls_token": true,
+    "pooling_mode_mean_tokens": false,
+    "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 +1,450 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:20554
+- loss:MultipleNegativesRankingLoss
+base_model: AITeamVN/Vietnamese_Embedding_v2
+widget:
+- source_sentence: bon
+  sentences:
+  - cây mon
+  - đổ chậu nước
+  - yên phận làm ăn
+- source_sentence: Tua cáy chọt oóc khói doòng
+  sentences:
+  - chăn
+  - hen thở khò khè
+  - con gà xổng ra khỏi lồng
+- source_sentence: Khảm
+  sentences:
+  - kiểm tra
+  - treo
+  - rạo rực
+- source_sentence: khẩu hảo Bẩu
+  sentences:
+  - mẹ mắng không bằng bố sa sầm mặt
+  - cạo trọc đầu
+  - thóc chưa khô hẳn
+- source_sentence: Các
+  sentences:
+  - mập mạp
+  - chân tay mập
+  - bắc
+datasets:
+- HeyDunaX/tay-vietnamese-nmt
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on AITeamVN/Vietnamese_Embedding_v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [AITeamVN/Vietnamese_Embedding_v2](https://huggingface.co/AITeamVN/Vietnamese_Embedding_v2) on the [tay-vietnamese-nmt](https://huggingface.co/datasets/HeyDunaX/tay-vietnamese-nmt) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [AITeamVN/Vietnamese_Embedding_v2](https://huggingface.co/AITeamVN/Vietnamese_Embedding_v2) <!-- at revision 18b44161e041bf1d3a333ab5144b5b7b93f914d2 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [tay-vietnamese-nmt](https://huggingface.co/datasets/HeyDunaX/tay-vietnamese-nmt)
+<!-- - **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': 'XLMRobertaModel'})
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("HeyDunaX/Tay_Embedding")
+# Run inference
+sentences = [
+    'Các',
+    'bắc',
+    'chân tay mập',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[ 1.0000,  0.3147, -0.0254],
+#         [ 0.3147,  1.0000, -0.1489],
+#         [-0.0254, -0.1489,  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.*
+-->
+
+<!--
+## 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
+
+#### tay-vietnamese-nmt
+
+* Dataset: [tay-vietnamese-nmt](https://huggingface.co/datasets/HeyDunaX/tay-vietnamese-nmt) at [2b04e13](https://huggingface.co/datasets/HeyDunaX/tay-vietnamese-nmt/tree/2b04e139b670d8ad62693d1fbdb943940e4acc05)
+* Size: 20,554 training samples
+* Columns: <code>sentence1</code> and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                        |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 6.77 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.85 tokens</li><li>max: 17 tokens</li></ul> |
+* Samples:
+  | sentence1                 | sentence2               |
+  |:--------------------------|:------------------------|
+  | <code>me</code>           | <code>bà cô</code>      |
+  | <code>noọng ấc cải</code> | <code>em ngực bự</code> |
+  | <code>noọng</code>        | <code>em gái</code>     |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Evaluation Dataset
+
+#### tay-vietnamese-nmt
+
+* Dataset: [tay-vietnamese-nmt](https://huggingface.co/datasets/HeyDunaX/tay-vietnamese-nmt) at [2b04e13](https://huggingface.co/datasets/HeyDunaX/tay-vietnamese-nmt/tree/2b04e139b670d8ad62693d1fbdb943940e4acc05)
+* Size: 2,295 evaluation samples
+* Columns: <code>sentence1</code> and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                        |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 7.24 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.02 tokens</li><li>max: 22 tokens</li></ul> |
+* Samples:
+  | sentence1                            | sentence2                                   |
+  |:-------------------------------------|:--------------------------------------------|
+  | <code>Hết fiệc ác</code>             | <code>làm việc khoẻ</code>                  |
+  | <code>slấc ác</code>                 | <code>giặc độc ác</code>                    |
+  | <code>ái chin mác rèo năm mạy</code> | <code>Muốn ăn quả thì phải trồng cây</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `gradient_accumulation_steps`: 4
+- `learning_rate`: 1e-05
+- `num_train_epochs`: 10
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0.1
+- `fp16`: True
+- `load_best_model_at_end`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `per_device_eval_batch_size`: 8
+- `gradient_accumulation_steps`: 4
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 1e-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`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0.1
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `enable_jit_checkpoint`: False
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `use_cpu`: False
+- `seed`: 42
+- `data_seed`: None
+- `bf16`: False
+- `fp16`: True
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: -1
+- `ddp_backend`: None
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `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
+- `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
+- `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_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `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
+- `use_cache`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.1556 | 100  | 1.7414        | -               |
+| 0.3113 | 200  | 1.3566        | -               |
+| 0.4669 | 300  | 1.1332        | -               |
+| 0.6226 | 400  | 1.0198        | -               |
+| 0.7782 | 500  | 0.8943        | -               |
+| 0.9339 | 600  | 0.7909        | -               |
+| 1.0    | 643  | -             | 0.7135          |
+| 1.0887 | 700  | 0.7070        | -               |
+| 1.2444 | 800  | 0.6029        | -               |
+| 1.4    | 900  | 0.6095        | -               |
+| 1.5556 | 1000 | 0.5436        | -               |
+| 1.7113 | 1100 | 0.5534        | -               |
+| 1.8669 | 1200 | 0.5363        | -               |
+| 2.0    | 1286 | -             | 0.5121          |
+| 2.0218 | 1300 | 0.4886        | -               |
+| 2.1774 | 1400 | 0.3853        | -               |
+| 2.3331 | 1500 | 0.3940        | -               |
+| 2.4887 | 1600 | 0.3859        | -               |
+| 2.6444 | 1700 | 0.4035        | -               |
+| 2.8    | 1800 | 0.3686        | -               |
+| 2.9556 | 1900 | 0.3662        | -               |
+| 3.0    | 1929 | -             | 0.4505          |
+| 3.1105 | 2000 | 0.3276        | -               |
+| 3.2661 | 2100 | 0.2877        | -               |
+| 3.4218 | 2200 | 0.2991        | -               |
+| 3.5774 | 2300 | 0.2898        | -               |
+| 3.7331 | 2400 | 0.2704        | -               |
+| 3.8887 | 2500 | 0.2807        | -               |
+| 4.0    | 2572 | -             | 0.4247          |
+| 4.0436 | 2600 | 0.2879        | -               |
+| 4.1992 | 2700 | 0.2300        | -               |
+| 4.3549 | 2800 | 0.2233        | -               |
+| 4.5105 | 2900 | 0.2169        | -               |
+| 4.6661 | 3000 | 0.2273        | -               |
+| 4.8218 | 3100 | 0.2149        | -               |
+| 4.9774 | 3200 | 0.2277        | -               |
+| 5.0    | 3215 | -             | 0.4163          |
+| 5.1323 | 3300 | 0.1973        | -               |
+| 5.2879 | 3400 | 0.1856        | -               |
+| 5.4436 | 3500 | 0.1686        | -               |
+| 5.5992 | 3600 | 0.1797        | -               |
+| 5.7549 | 3700 | 0.1830        | -               |
+| 5.9105 | 3800 | 0.1701        | -               |
+| 6.0    | 3858 | -             | 0.4066          |
+| 6.0654 | 3900 | 0.1620        | -               |
+| 6.2210 | 4000 | 0.1453        | -               |
+| 6.3767 | 4100 | 0.1593        | -               |
+| 6.5323 | 4200 | 0.1481        | -               |
+| 6.6879 | 4300 | 0.1506        | -               |
+| 6.8436 | 4400 | 0.1534        | -               |
+| 6.9992 | 4500 | 0.1554        | -               |
+| 7.0    | 4501 | -             | 0.3907          |
+| 7.1541 | 4600 | 0.1284        | -               |
+| 7.3097 | 4700 | 0.1266        | -               |
+| 7.4654 | 4800 | 0.1392        | -               |
+| 7.6210 | 4900 | 0.1292        | -               |
+| 7.7767 | 5000 | 0.1309        | -               |
+| 7.9323 | 5100 | 0.1318        | -               |
+| 8.0    | 5144 | -             | 0.3922          |
+| 8.0872 | 5200 | 0.1263        | -               |
+| 8.2428 | 5300 | 0.1136        | -               |
+| 8.3984 | 5400 | 0.1161        | -               |
+| 8.5541 | 5500 | 0.1137        | -               |
+| 8.7097 | 5600 | 0.1231        | -               |
+| 8.8654 | 5700 | 0.1187        | -               |
+| 9.0    | 5787 | -             | 0.3875          |
+| 9.0202 | 5800 | 0.1182        | -               |
+| 9.1759 | 5900 | 0.1059        | -               |
+| 9.3315 | 6000 | 0.1062        | -               |
+| 9.4872 | 6100 | 0.1044        | -               |
+| 9.6428 | 6200 | 0.0992        | -               |
+| 9.7984 | 6300 | 0.1057        | -               |
+| 9.9541 | 6400 | 0.1048        | -               |
+| 10.0   | 6430 | -             | 0.3878          |
+
+
+### Framework Versions
+- Python: 3.12.12
+- Sentence Transformers: 5.2.2
+- Transformers: 5.0.0
+- PyTorch: 2.9.0+cu126
+- Accelerate: 1.12.0
+- Datasets: 4.0.0
+- Tokenizers: 0.22.2
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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 +1,30 @@
+{
+  "add_cross_attention": false,
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "is_decoder": false,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.2.2",
+    "transformers": "5.0.0",
+    "pytorch": "2.9.0+cu126"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7796a2a71dd43d0034d3b4a1b0ce68f2e85c6c43c1d607a57c515e83131ac452
+size 2271064408

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 8192,
+    "do_lower_case": false
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bc552f2faa9ccaf0be4136806b77d442c6f81061a20ce3c569927d3908599284
+size 16781485

tokenizer_config.json

@@ -0,0 +1,17 @@
+{
+  "add_prefix_space": true,
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "is_local": true,
+  "mask_token": "<mask>",
+  "model_max_length": 8192,
+  "model_specific_special_tokens": {},
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}