Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
English
t5
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
prompt-retrieval
text-reranking
feature-extraction
English
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
Eval Results (legacy)
Instructions to use ahmet1338/finetuned_embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ahmet1338/finetuned_embedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ahmet1338/finetuned_embedder") 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 ahmet1338/finetuned_embedder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ahmet1338/finetuned_embedder") model = AutoModel.from_pretrained("ahmet1338/finetuned_embedder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c89a4f413c48c4aa284f708111ee18b1f76792d615d7cea694b0ff18e066958
- Size of remote file:
- 1.34 GB
- SHA256:
- a04766ba893b976c1be48211bb11b34b919f39cb36f4343e3d969faee8326a3a
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