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
| { | |
| "_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, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.49.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 31102 | |
| } | |