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
File size: 731 Bytes
d597215 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | epoch,steps,accuracy_cosinus,accuracy_manhattan,accuracy_euclidean
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0,2000,0.9926050420168068,0.9926050420168068,0.9929411764705882
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0,-1,0.9963025210084033,0.9966386554621849,0.9966386554621849
1,1000,0.9976470588235294,0.9976470588235294,0.9973109243697479
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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
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