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
File size: 327 Bytes
1629152 bf92763 | 1 2 3 4 5 6 7 8 9 10 11 | {
"name": "TinyE5-L6-384",
"embedding_dimension": 384,
"pooling": "mean",
"normalize": true,
"query_prefix": "query: ",
"passage_prefix": "passage: ",
"base_model": "sentence-transformers/all-MiniLM-L6-v2",
"training_dataset": "sentence-transformers/msmarco-bm25/triplet",
"train_last_n_layers": 2
} |