
# kornwtp/ConGen-WangchanBERT-Small

This is a [ConGen](https://github.com/KornWtp/ConGen) model: It maps sentences to a 128 dimensional dense vector space and can be used for tasks like semantic search.



## Usage

Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed:

```
pip install -U git+https://github.com/KornWtp/ConGen.git
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["กลุ่มผู้ชายเล่นฟุตบอลบนชายหาด", "กลุ่มเด็กชายกำลังเล่นฟุตบอลบนชายหาด"]

model = SentenceTransformer('kornwtp/ConGen-WangchanBERT-Small')
embeddings = model.encode(sentences)
print(embeddings)
```



## Evaluation Results



For an automated evaluation of this model, see the *Thai Sentence Embeddings Benchmark*: [Semantic Textual Similarity](https://github.com/KornWtp/ConGen#thai-semantic-textual-similarity-benchmark)


## Citing & Authors

```bibtex 
@inproceedings{limkonchotiwat-etal-2022-congen,
    title = "{ConGen}: Unsupervised Control and Generalization Distillation For Sentence Representation",
    author = "Limkonchotiwat, Peerat  and
      Ponwitayarat, Wuttikorn  and
      Lowphansirikul, Lalita and
      Udomcharoenchaikit, Can  and
      Chuangsuwanich, Ekapol  and
      Nutanong, Sarana",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    year = "2022",
    publisher = "Association for Computational Linguistics",
}
```
{"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch bert feature-extraction sentence-similarity transformers license:apache-2.0 endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us 


# kornwtp/ConGen-WangchanBERT-Small

This is a ConGen model: It maps sentences to a 128 dimensional dense vector space and can be used for tasks like semantic search.



## Usage

Using this model becomes easy when you have ConGen installed:



Then you can use the model like this:





## Evaluation Results



For an automated evaluation of this model, see the *Thai Sentence Embeddings Benchmark*: Semantic Textual Similarity


## Citing & Authors


# kornwtp/ConGen-WangchanBERT-Small

This is a ConGen model: It maps sentences to a 128 dimensional dense vector space and can be used for tasks like semantic search. ## Usage

Using this model becomes easy when you have ConGen installed:



Then you can use the model like this: ## Evaluation Results



For an automated evaluation of this model, see the *Thai Sentence Embeddings Benchmark*: Semantic Textual Similarity ## Citing & Authors
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us 
 # kornwtp/ConGen-WangchanBERT-Small

This is a ConGen model: It maps sentences to a 128 dimensional dense vector space and can be used for tasks like semantic search. ## Usage

Using this model becomes easy when you have ConGen installed:



Then you can use the model like this: ## Evaluation Results



For an automated evaluation of this model, see the *Thai Sentence Embeddings Benchmark*: Semantic Textual Similarity ## Citing & Authors