Sentence Similarity
sentence-transformers
Safetensors
Transformers
German
bert
feature-extraction
text-embeddings-inference
Instructions to use lwolfrum2/careerbert-g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lwolfrum2/careerbert-g with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lwolfrum2/careerbert-g") 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 lwolfrum2/careerbert-g with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lwolfrum2/careerbert-g") model = AutoModel.from_pretrained("lwolfrum2/careerbert-g") - Notebooks
- Google Colab
- Kaggle
File size: 799 Bytes
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"_name_or_path": "C:\\Users\\Lukas\\OneDrive\\Dokumente\\Uni\\Master IIS\\4. Semester\\Masterarbeit_Docs\\Masterarbeit_LWo\\00_data\\SBERT_Models\\models\\gbert_batch32_woTSDAE_2e-05_f10",
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
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"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.49.0",
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}
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