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--- |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- transformers |
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language: |
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- vi |
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--- |
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# NghiemAbe/Vi-Legal-Bi-Encoder-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. |
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<!--- Describe your model here --> |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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from pyvi.ViTokenizer import tokenize |
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sentences = [tokenize("This is an example sentence"), tokenize("Each sentence is converted")] |
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model = SentenceTransformer('NghiemAbe/Vi-Legal-Bi-Encoder-v2') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Usage (HuggingFace Transformers) |
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Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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#Mean Pooling - Take attention mask into account for correct averaging |
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def mean_pooling(model_output, attention_mask): |
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings |
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
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# Sentences we want sentence embeddings for |
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sentences = [tokenize("This is an example sentence"), tokenize("Each sentence is converted")] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained('NghiemAbe/Vi-Legal-Bi-Encoder-v2') |
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model = AutoModel.from_pretrained('NghiemAbe/Vi-Legal-Bi-Encoder-v2') |
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# Tokenize sentences |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
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# Compute token embeddings |
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with torch.no_grad(): |
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model_output = model(**encoded_input) |
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# Perform pooling. In this case, mean pooling. |
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
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print("Sentence embeddings:") |
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print(sentence_embeddings) |
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``` |
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## Evaluation Results |
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I evaluated my [Dev-Legal-Dataset](https://huggingface.co/datasets/NghiemAbe/dev_legal) and here are the results: |
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| Model | R@1 | R@5 | R@10 | R@20 | R@100 | MRR@5 | MRR@10 | MRR@20 | MRR@100 | Avg | |
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|------------------------------------------------------------------------|------|------|------|------|-------|-------|--------|--------|---------|------| |
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| keepitreal/vietnamese-sbert | 0.278| 0.552| 0.649| 0.734| 0.842 | 0.396 | 0.409 | 0.415 | 0.417 | 0.521| |
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| sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | 0.314| 0.486| 0.585| 0.662| 0.854 | 0.395 | 0.409 | 0.414 | 0.419 | 0.504| |
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| sentence-transformers/paraphrase-multilingual-mpnet-base-v2 | 0.354| 0.553| 0.646| 0.750| 0.896 | 0.449 | 0.461 | 0.468 | 0.472 | 0.561| |
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| intfloat/multilingual-e5-small | 0.488| 0.746| 0.835| 0.906| 0.962 | 0.610 | 0.620 | 0.624 | 0.625 | 0.713| |
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| intfloat/multilingual-e5-base | 0.466| 0.740| 0.840| 0.907| 0.952 | 0.596 | 0.608 | 0.612 | 0.613 | 0.704| |
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| bkai-foundation-models/vietnamese-bi-encoder | 0.644| 0.881| 0.924| 0.954| 0.986 | 0.752 | 0.757 | 0.758 | 0.759 | 0.824| |
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| Vi-Legal-Bi-Encoder-v2 | 0.720| 0.884| 0.935| 0.963| 0.986 | 0.796 | 0.802 | 0.803 | 0.804 | 0.855| |
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