Sentence Similarity
sentence-transformers
Safetensors
Burmese
bert
feature-extraction
dense
Generated from Trainer
myanmar
burmese
nlp
text-embeddings-inference
Instructions to use DatarrX/myX-Semantic-Light with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DatarrX/myX-Semantic-Light with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DatarrX/myX-Semantic-Light") sentences = [ "▁ထို့ကြောင့် ကြော်ငြာ ရှင် သည် နှိပ် လိုက်ပါ ကသာ ပေးချေ လိမ့်မည်။", "▁ကိုယ်ပိုင် စိတ်ကူး ဉာဏ် ဖြင့် ▁တီထွင် ရေးသား နိုင်သည်။", "▁ထိုအရာ အားလုံးက ▁အလွန် စိတ်လေး စရာ၊ ▁ကြောက်စရာကောင်း လှ သည်ဟု ▁ခံစား မိသည်။" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 384, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1536, | |
| "is_decoder": false, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.0.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 250037 | |
| } | |