kornwtp/simcse-model-m-bert-thai-cased

Model

1

stars

6

commits

2

linked in READMEs

Mar 7, 2024

updated

bert
endpoints_compatible
feature-extraction
pytorch
sentence-similarity
sentence-transformers
text-embeddings-inference
transformers
Browse cluster: Semantic Search & Sentence Embeddings

README

{kornwtp/simcse-model-m-bert-thai-cased}

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:

pip install -U sentence-transformers

Then you can use the model like this:

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

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

Contributors

kornwtp

6 commits

kornwtp/simcse-model-m-bert-thai-cased

Model

1

stars

6

commits

2

linked in READMEs

Mar 7, 2024

updated

bert
endpoints_compatible
feature-extraction
pytorch
sentence-similarity
sentence-transformers
text-embeddings-inference
transformers
Browse cluster: Semantic Search & Sentence Embeddings

README

{kornwtp/simcse-model-m-bert-thai-cased}

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:

pip install -U sentence-transformers

Then you can use the model like this:

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

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

Contributors

kornwtp

6 commits