Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use adit94/sentenceTest_kbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use adit94/sentenceTest_kbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("adit94/sentenceTest_kbert") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use adit94/sentenceTest_kbert with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("adit94/sentenceTest_kbert") model = AutoModel.from_pretrained("adit94/sentenceTest_kbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| epoch,steps,accuracy_cosinus,accuracy_manhattan,accuracy_euclidean | |
| 0,1000,0.9828571428571429,0.9825210084033613,0.9825210084033613 | |
| 0,2000,0.9926050420168068,0.9926050420168068,0.9929411764705882 | |
| 0,3000,0.9946218487394958,0.9952941176470588,0.9956302521008403 | |
| 0,-1,0.9963025210084033,0.9966386554621849,0.9966386554621849 | |
| 1,1000,0.9976470588235294,0.9976470588235294,0.9973109243697479 | |
| 1,2000,0.998655462184874,0.9989915966386554,0.9989915966386554 | |
| 1,3000,0.999327731092437,0.999327731092437,0.999327731092437 | |
| 1,-1,0.9989915966386554,0.999327731092437,0.999327731092437 | |
| 2,1000,0.9996638655462184,1.0,0.9996638655462184 | |
| 2,2000,0.9996638655462184,0.9996638655462184,0.9996638655462184 | |
| 2,3000,1.0,1.0,1.0 | |
| 2,-1,1.0,1.0,1.0 | |