---
language:
- vi
pipeline_tag: sentence-similarity
---
# NghiemAbe/sami-sbert-CT

🐱 <a href="https://github.com/nguyenvannghiem0312/SAMI_Q-A" target="_blank">Github Repo</a>

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.

I use pretrained model [bkai-foundation-models/vietnamese-bi-encoder](https://huggingface.co/bkai-foundation-models/vietnamese-bi-encoder) and train the model on SAMI dataset.

<!--- 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

# INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
sentences = ["Cô ấy là một người vui_tính .", "Cô ấy cười nói suốt cả ngày ."]

model = SentenceTransformer('NghiemAbe/sami-sbert-CT')
embeddings = model.encode(sentences)
print(embeddings)
```

## Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
  (1): Pooling({'word_embedding_dimension': 768, '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})
)
```