| | --- |
| | pipeline_tag: sentence-similarity |
| | tags: |
| | - sentence-transformers |
| | - feature-extraction |
| | - sentence-similarity |
| | - transformers |
| | --- |
| | |
| | # {mrp/simcse-model-distil-m-bert} |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) by using m-Distil-BERT as the baseline model 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) 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) |
| | ``` |