Add: commit model

.ipynb_checkpoints/README-checkpoint.md

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+---
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- sentence-embedding
+license: apache-2.0
+language:
+- fr
+metrics:
+- pearsonr
+- spearmanr
+---
+
+# [bilingual-embedding-large](https://huggingface.co/Lajavaness/bilingual-embedding-large)
+
+bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [BGE M3](https://huggingface.co/BAAI/bge-m3), a pre-trained language model larged on the [BGE M3](https://huggingface.co/BAAI/bge-m3) architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BilingualModel 
+  (1): Pooling({'word_embedding_dimension': 1024, '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})
+  (2): Normalize()
+)
+```
+
+## Training and Fine-tuning process
+#### Stage 1: NLI Training
+- Dataset: [(SNLI+XNLI) for english+french]
+- Method: Training using Multi-Negative Ranking Loss. This stage focused on improving the model's ability to discern and rank nuanced differences in sentence semantics.
+### Stage 3: Continued Fine-tuning for Semantic Textual Similarity on STS Benchmark
+- Dataset: [STSB-fr and en]
+- Method: Fine-tuning specifically for the semantic textual similarity benchmark using Siamese BERT-Networks configured with the 'sentence-transformers' library. 
+### Stage 4: Advanced Augmentation Fine-tuning
+- Dataset: STSB-vn with generate [silver sample from gold sample](https://www.sbert.net/examples/training/data_augmentation/README.html)
+- Method: Employed an advanced strategy using [Augmented SBERT](https://arxiv.org/abs/2010.08240) with Pair Sampling Strategies, integrating both Cross-Encoder and Bi-Encoder models. This stage further refined the embeddings by enriching the training data dynamically, enhancing the model's robustness and accuracy.
+
+
+## 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
+from pyvi.ViTokenizer import tokenize
+
+sentences = ["Paris est une capitale de la France", "Paris is a capital of France"]
+
+model = SentenceTransformer('Lajavaness/bilingual-embedding-large', trust_remote_code=True)
+print(embeddings)
+
+```
+
+
+
+
+
+## Evaluation
+
+TODO
+
+## Citation
+    @article{chen2024bge,
+      title={Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation},
+      author={Chen, Jianlv and Xiao, Shitao and Zhang, Peitian and Luo, Kun and Lian, Defu and Liu, Zheng},
+      journal={arXiv preprint arXiv:2402.03216},
+      year={2024}
+    }
+
+    @article{conneau2019unsupervised,
+      title={Unsupervised cross-lingual representation learning at scale},
+      author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin},
+      journal={arXiv preprint arXiv:1911.02116},
+      year={2019}
+    }
+
+	@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{thakur2020augmented,
+      title={Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks},
+      author={Thakur, Nandan and Reimers, Nils and Daxenberger, Johannes and Gurevych, Iryna},
+      journal={arXiv e-prints},
+      pages={arXiv--2010},
+      year={2020}

1_Pooling/config.json

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+{
+  "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

@@ -1,3 +1,100 @@
----
-license: apache-2.0
----
+---
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- sentence-embedding
+license: apache-2.0
+language:
+- fr
+metrics:
+- pearsonr
+- spearmanr
+---
+
+# [bilingual-embedding-large](https://huggingface.co/Lajavaness/bilingual-embedding-large)
+
+bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [BGE M3](https://huggingface.co/BAAI/bge-m3), a pre-trained language model larged on the [BGE M3](https://huggingface.co/BAAI/bge-m3) architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BilingualModel 
+  (1): Pooling({'word_embedding_dimension': 1024, '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})
+  (2): Normalize()
+)
+```
+
+## Training and Fine-tuning process
+#### Stage 1: NLI Training
+- Dataset: [(SNLI+XNLI) for english+french]
+- Method: Training using Multi-Negative Ranking Loss. This stage focused on improving the model's ability to discern and rank nuanced differences in sentence semantics.
+### Stage 3: Continued Fine-tuning for Semantic Textual Similarity on STS Benchmark
+- Dataset: [STSB-fr and en]
+- Method: Fine-tuning specifically for the semantic textual similarity benchmark using Siamese BERT-Networks configured with the 'sentence-transformers' library. 
+### Stage 4: Advanced Augmentation Fine-tuning
+- Dataset: STSB-vn with generate [silver sample from gold sample](https://www.sbert.net/examples/training/data_augmentation/README.html)
+- Method: Employed an advanced strategy using [Augmented SBERT](https://arxiv.org/abs/2010.08240) with Pair Sampling Strategies, integrating both Cross-Encoder and Bi-Encoder models. This stage further refined the embeddings by enriching the training data dynamically, enhancing the model's robustness and accuracy.
+
+
+## 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
+from pyvi.ViTokenizer import tokenize
+
+sentences = ["Paris est une capitale de la France", "Paris is a capital of France"]
+
+model = SentenceTransformer('Lajavaness/bilingual-embedding-large', trust_remote_code=True)
+print(embeddings)
+
+```
+
+
+
+
+
+## Evaluation
+
+TODO
+
+## Citation
+    @article{chen2024bge,
+      title={Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation},
+      author={Chen, Jianlv and Xiao, Shitao and Zhang, Peitian and Luo, Kun and Lian, Defu and Liu, Zheng},
+      journal={arXiv preprint arXiv:2402.03216},
+      year={2024}
+    }
+
+    @article{conneau2019unsupervised,
+      title={Unsupervised cross-lingual representation learning at scale},
+      author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin},
+      journal={arXiv preprint arXiv:1911.02116},
+      year={2019}
+    }
+
+	@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{thakur2020augmented,
+      title={Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks},
+      author={Thakur, Nandan and Reimers, Nils and Daxenberger, Johannes and Gurevych, Iryna},
+      journal={arXiv e-prints},
+      pages={arXiv--2010},
+      year={2020}

config.json

@@ -0,0 +1,37 @@
+{
+  "_name_or_path": "dangvantuan/bilingual_impl",
+  "architectures": [
+    "BilingualModel"
+  ],
+  "model_type": "bilingual",
+  "auto_map": {
+    "AutoConfig":"dangvantuan/bilingual_impl--config.BilingualConfig",
+    "AutoModel": "dangvantuan/bilingual_impl--modeling.BilingualModel",
+    "AutoModelForMaskedLM": "dangvantuan/bilingual_impl--modeling.BilingualForMaskedLM",
+    "AutoModelForMultipleChoice": "dangvantuan/bilingual_impl--modeling.BilingualForMultipleChoice",
+    "AutoModelForQuestionAnswering": "dangvantuan/bilingual_impl--modeling.BilingualForQuestionAnswering",
+    "AutoModelForSequenceClassification": "dangvantuan/bilingual_impl--modeling.BilingualForSequenceClassification",
+    "AutoModelForTokenClassification": "dangvantuan/bilingual_impl--modeling.BilingualForTokenClassification"
+  },
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float16",
+  "transformers_version": "4.38.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,9 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.6.1",
+    "transformers": "4.39.3",
+    "pytorch": "2.1.2+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

model.safetensors

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+oid sha256:62c57a7185eefb9ad3648fb4a93e334ffa6ffcffa8247386c38e4c705d4a8449
+size 2271064456

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
+}

sentencepiece.bpe.model

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+size 5069051

special_tokens_map.json

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tokenizer_config.json

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+}