Sentence Similarity
sentence-transformers
PyTorch
Rust
ONNX
Safetensors
OpenVINO
Transformers
English
roberta
fill-mask
feature-extraction
text-embeddings-inference
Instructions to use novelcore/model7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model7 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model7") 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 novelcore/model7 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("novelcore/model7") model = AutoModelForMaskedLM.from_pretrained("novelcore/model7") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7b8a53fee773d73781ae9edb925772e52cf5d9d78c1a5ce3a4676f70b03cfe3
- Size of remote file:
- 326 MB
- SHA256:
- aa55f453a76f16b81368aacf2aaaf5256eb2d5fadae16453ecd9a1b32a8a8356
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