# {mrp/simcse-model-wangchanberta}

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.

<!--- Describe your model here -->
We use SimCSE [here](https://arxiv.org/pdf/2104.08821.pdf) by using mBERT as the baseline model and training the model with Thai Wikipedia [here](https://github.com/PyThaiNLP/ThaiWiki-clean/releases/tag/20210620?fbclid=IwAR1YcmZkb-xd1ibTWCJOcu98_FQ5x3ioZaGW1ME-VHy9fAQLhEr5tXTJygA)

## 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 = ["ฉันนะคือคนรักชาติยังไงละ!", "พวกสามกีบล้มเจ้า!"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
{}
transformers pytorch camembert feature-extraction arxiv:2104.08821 endpoints_compatible region:us
TAGS
#transformers #pytorch #camembert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #region-us 

# {mrp/simcse-model-wangchanberta}

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.


We use SimCSE here by using mBERT as the baseline model and training the model with Thai Wikipedia here

## Usage (Sentence-Transformers)

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



Then you can use the model like this:


# {mrp/simcse-model-wangchanberta}

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.


We use SimCSE here by using mBERT as the baseline model and training the model with Thai Wikipedia here ## Usage (Sentence-Transformers)

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



Then you can use the model like this:
TAGS
#transformers #pytorch #camembert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #region-us 
 # {mrp/simcse-model-wangchanberta}

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.


We use SimCSE here by using mBERT as the baseline model and training the model with Thai Wikipedia here ## Usage (Sentence-Transformers)

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



Then you can use the model like this:
passage: TAGS
#transformers #pytorch #camembert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #region-us 
# {mrp/simcse-model-wangchanberta}

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.


We use SimCSE here by using mBERT as the baseline model and training the model with Thai Wikipedia here## Usage (Sentence-Transformers)

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



Then you can use the model like this: