Sentence Similarity
sentence-transformers
ONNX
Transformers
setfit
German
bert
feature-extraction
text-embeddings-inference
Instructions to use blackcodetavern/gbert-large-paraphrase-cosine-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use blackcodetavern/gbert-large-paraphrase-cosine-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("blackcodetavern/gbert-large-paraphrase-cosine-onnx") sentences = [ "Das ist eine glückliche Person", "Das ist ein glücklicher Hund", "Das ist eine sehr glückliche Person", "Heute ist ein sonniger Tag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use blackcodetavern/gbert-large-paraphrase-cosine-onnx with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("blackcodetavern/gbert-large-paraphrase-cosine-onnx") model = AutoModel.from_pretrained("blackcodetavern/gbert-large-paraphrase-cosine-onnx", device_map="auto") - setfit
How to use blackcodetavern/gbert-large-paraphrase-cosine-onnx with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("blackcodetavern/gbert-large-paraphrase-cosine-onnx") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download config.json from blackcodetavern/gbert-large-paraphrase-cosine-onnx: direct link, hf CLI and curl.
- Browser
- Download file 625 Bytes
-
https://huggingface.co/blackcodetavern/gbert-large-paraphrase-cosine-onnx/resolve/main/config.json
- Command line
-
hf download hf://blackcodetavern/gbert-large-paraphrase-cosine-onnx/config.json
-
curl -L -o config.json https://huggingface.co/blackcodetavern/gbert-large-paraphrase-cosine-onnx/resolve/main/config.json
625 Bytes
| { | |
| "_name_or_path": "deutsche-telekom/gbert-large-paraphrase-cosine", | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.31.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 31102 | |
| } | |