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": 768,
+  "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 +1,136 @@
+---
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- phobert
+- vietnamese
+- sentence-embedding
+license: apache-2.0
+language:
+- vi
+metrics:
+- pearsonr
+- spearmanr
+---
+## Model Description:
+[**vietnamese-document-embedding**](https://huggingface.co/dangvantuan/vietnamese-document-embedding) is the Document Embedding Model for Vietnamese language  with context length up to 8096 tokens. This model is a specialized long text-embedding trained specifically for the Vietnamese language, which is built upon [gte-multilingual](Alibaba-NLP/gte-multilingual-base) and trained using the Multi-Negative Ranking Loss, Matryoshka2dLoss and SimilarityLoss.
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: VietnameseModel 
+  (1): Pooling({'word_embedding_dimension': 768, '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()
+)
+```
+## Training and Fine-tuning process
+The model underwent a rigorous four-stage training and fine-tuning process, each tailored to enhance its ability to generate precise and contextually relevant sentence embeddings for the Vietnamese language. Below is an outline of these stages:
+#### Stage 1: Training NLI on dataset XNLI: 
+- Dataset: [XNLI-vn ](https://huggingface.co/datasets/xnli/viewer/vi)
+- Method: Training using Multi-Negative Ranking Loss and Matryoshka2dLoss. This stage focused on improving the model's ability to discern and rank nuanced differences in sentence semantics.
+### Stage 2: Fine-tuning for Semantic Textual Similarity on STS Benchmark
+- Dataset: [STSB-vn](https://huggingface.co/datasets/doanhieung/vi-stsbenchmark)
+- Method: Fine-tuning specifically for the semantic textual similarity benchmark using Siamese BERT-Networks configured with the 'sentence-transformers' library. This stage honed the model's precision in capturing semantic similarity across various types of Vietnamese texts.
+
+
+## Usage:
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["Hà Nội là thủ đô của Việt Nam", "Đà Nẵng là thành phố du lịch"]
+
+
+model = SentenceTransformer('dangvantuan/vietnamese-document-embedding', trust_remote_code=True)
+embeddings = model.encode(sentences)
+print(embeddings)
+
+```
+
+
+## Evaluation
+The model can be evaluated as follows on the [Vienamese data of stsb](https://huggingface.co/datasets/doanhieung/vi-stsbenchmark).
+
+```python
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.readers import InputExample
+from datasets import load_dataset
+def convert_dataset(dataset):
+    dataset_samples=[]
+    for df in dataset:
+        score = float(df['score'])/5.0  # Normalize score to range 0 ... 1
+        inp_example = InputExample(texts=[df['sentence1'], df['sentence2']], label=score)
+        dataset_samples.append(inp_example)
+    return dataset_samples
+
+# Loading the dataset for evaluation
+vi_sts = load_dataset("doanhieung/vi-stsbenchmark")["train"]
+df_dev = vi_sts.filter(lambda example: example['split'] == 'dev')
+df_test = vi_sts.filter(lambda example: example['split'] == 'test')
+
+# Convert the dataset for evaluation
+
+# For Dev set:
+dev_samples = convert_dataset(df_dev)
+val_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')
+val_evaluator(model, output_path="./")
+
+# For Test set:
+test_samples = convert_dataset(df_test)
+test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name='sts-test')
+test_evaluator(model, output_path="./")
+```
+
+
+
+
+### Metric for all dataset of [Semantic Textual Similarity on STS Benchmark](https://huggingface.co/datasets/anti-ai/ViSTS)
+
+**Spearman score**
+| Model                                                                                                               | [STSB]   | [STS12]| [STS13] | [STS14] | [STS15] |    [STS16] | [SICK] | Mean |
+|-----------------------------------------------------------|---------|----------|----------|----------|----------|----------|---------|--------|
+| [dangvantuan/vietnamese-embedding](https://huggingface.co/dangvantuan/vietnamese-embedding)                                                 |84.84|	79.04|	85.30|	81.38|	87.06|	79.95|	79.58|	82.45|
+| [dangvantuan/vietnamese-embedding-LongContext](https://huggingface.co/dangvantuan/vietnamese-embedding-LongContext)  |85.25|	75.77|	83.82|	81.69|	88.48|	81.5|	78.2|	82.10|
+
+## Citation
+
+
+	@article{reimers2019sentence,
+	   title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
+	   author={Nils Reimers, Iryna Gurevych},
+	   journal={https://arxiv.org/abs/1908.10084},
+	   year={2019}
+	}
+
+
+    @article{zhang2024mgte,
+      title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
+      author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others},
+      journal={arXiv preprint arXiv:2407.19669},
+      year={2024}
+    }
+    
+    @article{li2023towards,
+      title={Towards general text embeddings with multi-stage contrastive learning},
+      author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
+      journal={arXiv preprint arXiv:2308.03281},
+      year={2023}
+    }
+    
+    @article{li20242d,
+      title={2d matryoshka sentence embeddings},
+      author={Li, Xianming and Li, Zongxi and Li, Jing and Xie, Haoran and Li, Qing},
+      journal={arXiv preprint arXiv:2402.14776},
+      year={2024}
+    }

config.json

@@ -0,0 +1,50 @@
+{
+  "_name_or_path": "Chow05/fine-tune-embedding-v2",
+  "architectures": [
+    "VietnameseModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "dangvantuan/Vietnamese_impl--configuration.VietnameseConfig",
+    "AutoModel": "dangvantuan/Vietnamese_impl--modeling.VietnameseModel",
+    "AutoModelForMaskedLM": "dangvantuan/Vietnamese_impl--modeling.VietnameseForMaskedLM",
+    "AutoModelForMultipleChoice": "dangvantuan/Vietnamese_impl--modeling.VietnameseForMultipleChoice",
+    "AutoModelForQuestionAnswering": "dangvantuan/Vietnamese_impl--modeling.VietnameseForQuestionAnswering",
+    "AutoModelForSequenceClassification": "dangvantuan/Vietnamese_impl--modeling.VietnameseForSequenceClassification",
+    "AutoModelForTokenClassification": "dangvantuan/Vietnamese_impl--modeling.VietnameseForTokenClassification"
+  },
+  "classifier_dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "layer_norm_type": "layer_norm",
+  "logn_attention_clip1": false,
+  "logn_attention_scale": false,
+  "max_position_embeddings": 8192,
+  "model_type": "Vietnamese",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pack_qkv": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "rope",
+  "rope_scaling": {
+    "factor": 8.0,
+    "type": "ntk"
+  },
+  "rope_theta": 20000,
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.0",
+  "type_vocab_size": 1,
+  "unpad_inputs": false,
+  "use_memory_efficient_attention": false,
+  "vocab_size": 250048
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.0",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+size 1221487872

modules.json

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+[
+  {
+    "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

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+{
+  "max_seq_length": 8192,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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tokenizer_config.json

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+  "tokenizer_class": "XLMRobertaTokenizer",
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+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}