
# {MODEL_NAME}

This is a Vietnamese [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like questions answering or semantic search.


<!--- Describe your model here -->

## Usage (Sentence-Transformers)

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 = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```



## Usage (HuggingFace Transformers)
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.

```python
from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```



## Evaluation Results

<!--- Describe how your model was evaluated -->

The thesis will be available on [https://github.com/ncthuan/uet-qa](https://github.com/ncthuan/uet-qa) with evaluation results in chapter 4.

paraphrase-multilingual-minilm: 75 recall@10, 49 MRR@10

this model: 85 recall@10, 58 MRR@10

## Training

It was distilled using English-Vietnamese parallel data with this [training script](https://github.com/ncthuan/uet-qa/blob/main/scripts/train/make_multilingual.py)
that follows the work of [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://www.sbert.net/examples/training/multilingual/README.html)

teacher: msmarco-MiniLM-L12-cos-v5

student: paraphrase-multilingual-minilm-L12-v2

Data: PhoMT, MKQA, MLQA, XQuAD

The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 40148 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.MSELoss.MSELoss` 

Parameters of the fit()-Method:
```
{
    "epochs": 2,
    "evaluation_steps": 2000,
    "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "correct_bias": false,
        "eps": 1e-06,
        "lr": 1e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 2000,
    "weight_decay": 0.005
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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})
)
```

## Citing & Authors

<!--- Describe where people can find more information -->
```
@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}

@article{thuan2022-uetqa,
  title={{Extractive question answering system on regulations for University of Engineering and Technology}},
  author={Nguyen, Thuan},
  journal={Undergraduate Thesis, University of Engineering and Technology, Vietnam National University Hanoi},
  year={2022}
}
```
{"license": "mit", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch bert feature-extraction sentence-similarity transformers arxiv:2004.09813 license:mit endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-2004.09813 #license-mit #endpoints_compatible #region-us 


# {MODEL_NAME}

This is a Vietnamese sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like questions answering or semantic search.




## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this:





## Usage (HuggingFace Transformers)
Without sentence-transformers, 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.





## Evaluation Results



The thesis will be available on URL with evaluation results in chapter 4.

paraphrase-multilingual-minilm: 75 recall@10, 49 MRR@10

this model: 85 recall@10, 58 MRR@10

## Training

It was distilled using English-Vietnamese parallel data with this training script
that follows the work of Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

teacher: msmarco-MiniLM-L12-cos-v5

student: paraphrase-multilingual-minilm-L12-v2

Data: PhoMT, MKQA, MLQA, XQuAD

The model was trained with the parameters:

DataLoader:

'URL.dataloader.DataLoader' of length 40148 with parameters:


Loss:

'sentence_transformers.losses.MSELoss.MSELoss' 

Parameters of the fit()-Method:



## Full Model Architecture


## Citing & Authors



# {MODEL_NAME}

This is a Vietnamese sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like questions answering or semantic search. ## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this: ## Usage (HuggingFace Transformers)
Without sentence-transformers, 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. ## Evaluation Results



The thesis will be available on URL with evaluation results in chapter 4.

paraphrase-multilingual-minilm: 75 recall@10, 49 MRR@10

this model: 85 recall@10, 58 MRR@10 ## Training

It was distilled using English-Vietnamese parallel data with this training script
that follows the work of Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

teacher: msmarco-MiniLM-L12-cos-v5

student: paraphrase-multilingual-minilm-L12-v2

Data: PhoMT, MKQA, MLQA, XQuAD

The model was trained with the parameters:

DataLoader:

'URL.dataloader.DataLoader' of length 40148 with parameters:


Loss:

'sentence_transformers.losses.MSELoss.MSELoss' 

Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-2004.09813 #license-mit #endpoints_compatible #region-us 
 # {MODEL_NAME}

This is a Vietnamese sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like questions answering or semantic search. ## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this: ## Usage (HuggingFace Transformers)
Without sentence-transformers, 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. ## Evaluation Results



The thesis will be available on URL with evaluation results in chapter 4.

paraphrase-multilingual-minilm: 75 recall@10, 49 MRR@10

this model: 85 recall@10, 58 MRR@10 ## Training

It was distilled using English-Vietnamese parallel data with this training script
that follows the work of Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

teacher: msmarco-MiniLM-L12-cos-v5

student: paraphrase-multilingual-minilm-L12-v2

Data: PhoMT, MKQA, MLQA, XQuAD

The model was trained with the parameters:

DataLoader:

'URL.dataloader.DataLoader' of length 40148 with parameters:


Loss:

'sentence_transformers.losses.MSELoss.MSELoss' 

Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors
passage: TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-2004.09813 #license-mit #endpoints_compatible #region-us 
# {MODEL_NAME}

This is a Vietnamese sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like questions answering or semantic search.## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this:## Usage (HuggingFace Transformers)
Without sentence-transformers, 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.## Evaluation Results



The thesis will be available on URL with evaluation results in chapter 4.

paraphrase-multilingual-minilm: 75 recall@10, 49 MRR@10

this model: 85 recall@10, 58 MRR@10## Training

It was distilled using English-Vietnamese parallel data with this training script
that follows the work of Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

teacher: msmarco-MiniLM-L12-cos-v5

student: paraphrase-multilingual-minilm-L12-v2

Data: PhoMT, MKQA, MLQA, XQuAD

The model was trained with the parameters:

DataLoader:

'URL.dataloader.DataLoader' of length 40148 with parameters:


Loss:

'sentence_transformers.losses.MSELoss.MSELoss' 

Parameters of the fit()-Method:## Full Model Architecture## Citing & Authors