Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
bert
feature-extraction
semantic-search
custom-architecture
automated-tokenizer
Eval Results (legacy)
Instructions to use LNTTushar/tryn-mini-7m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LNTTushar/tryn-mini-7m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LNTTushar/tryn-mini-7m") 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 LNTTushar/tryn-mini-7m with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LNTTushar/tryn-mini-7m") model = AutoModel.from_pretrained("LNTTushar/tryn-mini-7m") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f7710150e8416c5e1dd254fcb3f7e2cc58d7296be31a053f1e2eacb2a662a690
- Size of remote file:
- 29.5 MB
- SHA256:
- 48e1d5052943677eefd33a90a6dcd05e22d273aa8b3825e65f3ddeddd623d5a9
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