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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("djspiewak/typelevel-bert") model = AutoModel.from_pretrained("djspiewak/typelevel-bert", device_map="auto") - 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
File size: 322 Bytes
1c75a25 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"backend": "tokenizers",
"cls_token": "[CLS]",
"do_lower_case": true,
"is_local": false,
"mask_token": "[MASK]",
"model_max_length": 512,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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