Feature Extraction
sentence-transformers
Safetensors
Transformers
English
bert
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use hsikchi/dump with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hsikchi/dump with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hsikchi/dump") 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] - Transformers
How to use hsikchi/dump with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hsikchi/dump")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hsikchi/dump") model = AutoModel.from_pretrained("hsikchi/dump") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c317479affed24e355867fb1a77a8c398eac1616f13b41c1f437a55babc43e6
- Size of remote file:
- 438 MB
- SHA256:
- 0d4db737f56aaea90796b5a8d219de0eee958295a575c611f6b417ad340151da
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