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
Download config.json from djspiewak/typelevel-bert: direct link, hf CLI and curl.
- Browser
- Download file 425 Bytes
-
https://huggingface.co/djspiewak/typelevel-bert/resolve/main/config.json
- Command line
-
hf download hf://djspiewak/typelevel-bert/config.json
-
curl -L -o config.json https://huggingface.co/djspiewak/typelevel-bert/resolve/main/config.json
425 Bytes
| { | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 256, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1024, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 4, | |
| "pad_token_id": 0, | |
| "type_vocab_size": 2, | |
| "vocab_size": 30522 | |
| } | |