Feature Extraction
Transformers
Safetensors
sentence-transformers
ONNX
English
bert
embeddings
text-embeddings
semantic-search
information-retrieval
int8
minilm
e5
text-embeddings-inference
Instructions to use GrowBitLabs/tinye5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GrowBitLabs/tinye5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="GrowBitLabs/tinye5")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("GrowBitLabs/tinye5") model = AutoModel.from_pretrained("GrowBitLabs/tinye5", device_map="auto") - sentence-transformers
How to use GrowBitLabs/tinye5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GrowBitLabs/tinye5") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e098a221b001b9d5c39d566ab3c1a3bd4c3e5da751592b0db063b3b489de8f7a
- Size of remote file:
- 90.9 MB
- SHA256:
- 2fb8de9bbf527353997deb64a4a6daee75da86a2eb9e711b825a6e7fa7ed2d01
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