Feature Extraction
Transformers
ONNX
Safetensors
sentence-transformers
Transformers.js
English
bert
text-embeddings
semantic-search
knowledge-distillation
Eval Results (legacy)
text-embeddings-inference
Instructions to use djspiewak/typelevel-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djspiewak/typelevel-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="djspiewak/typelevel-bert")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("djspiewak/typelevel-bert") model = AutoModel.from_pretrained("djspiewak/typelevel-bert") - sentence-transformers
How to use djspiewak/typelevel-bert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("djspiewak/typelevel-bert") 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.js
How to use djspiewak/typelevel-bert with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'djspiewak/typelevel-bert'); - Notebooks
- Google Colab
- Kaggle
Ctrl+K