Token Classification
Transformers
Safetensors
German
bert
ner
german
contact-information
imprint
web-extraction
Instructions to use andyP/gbert-base-contact-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andyP/gbert-base-contact-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="andyP/gbert-base-contact-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("andyP/gbert-base-contact-ner") model = AutoModelForTokenClassification.from_pretrained("andyP/gbert-base-contact-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from andyP/gbert-base-contact-ner: direct link, hf CLI and curl.
- Browser
- Download file 1.7 kB
-
https://huggingface.co/andyP/gbert-base-contact-ner/resolve/main/config.json
- Command line
-
hf download hf://andyP/gbert-base-contact-ner/config.json
-
curl -L -o config.json https://huggingface.co/andyP/gbert-base-contact-ner/resolve/main/config.json
1.7 kB
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-street", | |
| "2": "I-street", | |
| "3": "B-plz", | |
| "4": "I-plz", | |
| "5": "B-city", | |
| "6": "I-city", | |
| "7": "B-country", | |
| "8": "I-country", | |
| "9": "B-phone", | |
| "10": "I-phone", | |
| "11": "B-fax", | |
| "12": "I-fax", | |
| "13": "B-email", | |
| "14": "I-email", | |
| "15": "B-opening_hours", | |
| "16": "I-opening_hours", | |
| "17": "B-location_label", | |
| "18": "I-location_label", | |
| "19": "B-department", | |
| "20": "I-department", | |
| "21": "B-person", | |
| "22": "I-person" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": false, | |
| "label2id": { | |
| "B-city": 5, | |
| "B-country": 7, | |
| "B-department": 19, | |
| "B-email": 13, | |
| "B-fax": 11, | |
| "B-location_label": 17, | |
| "B-opening_hours": 15, | |
| "B-person": 21, | |
| "B-phone": 9, | |
| "B-plz": 3, | |
| "B-street": 1, | |
| "I-city": 6, | |
| "I-country": 8, | |
| "I-department": 20, | |
| "I-email": 14, | |
| "I-fax": 12, | |
| "I-location_label": 18, | |
| "I-opening_hours": 16, | |
| "I-person": 22, | |
| "I-phone": 10, | |
| "I-plz": 4, | |
| "I-street": 2, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.16.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 31102 | |
| } | |