Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use lunesco/bert-german-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lunesco/bert-german-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="lunesco/bert-german-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("lunesco/bert-german-ner") model = AutoModelForTokenClassification.from_pretrained("lunesco/bert-german-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "dbmdz/bert-base-german-cased", | |
| "architectures": [ | |
| "BertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-organization-company", | |
| "2": "B-location-route", | |
| "3": "B-trigger", | |
| "4": "B-location-stop", | |
| "5": "B-date", | |
| "6": "B-location-city", | |
| "7": "B-event-cause", | |
| "8": "I-event-cause", | |
| "9": "B-time", | |
| "10": "I-time", | |
| "11": "B-number", | |
| "12": "B-organization", | |
| "13": "I-organization", | |
| "14": "B-location-street", | |
| "15": "I-trigger", | |
| "16": "B-location", | |
| "17": "I-location", | |
| "18": "I-location-city", | |
| "19": "I-organization-company", | |
| "20": "B-duration", | |
| "21": "I-duration", | |
| "22": "I-location-street", | |
| "23": "I-location-stop", | |
| "24": "I-location-route", | |
| "25": "B-person", | |
| "26": "I-date", | |
| "27": "B-set", | |
| "28": "B-money", | |
| "29": "I-person", | |
| "30": "I-money", | |
| "31": "B-distance", | |
| "32": "I-distance", | |
| "33": "I-number", | |
| "34": "B-disaster-type", | |
| "35": "B-org-position", | |
| "36": "I-org-position", | |
| "37": "I-set", | |
| "38": "B-percent", | |
| "39": "I-percent", | |
| "40": "I-disaster-type" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "O": "0", | |
| "B-organization-company": "1", | |
| "B-location-route": "2", | |
| "B-trigger": "3", | |
| "B-location-stop": "4", | |
| "B-date": "5", | |
| "B-location-city": "6", | |
| "B-event-cause": "7", | |
| "I-event-cause": "8", | |
| "B-time": "9", | |
| "I-time": "10", | |
| "B-number": "11", | |
| "B-organization": "12", | |
| "I-organization": "13", | |
| "B-location-street": "14", | |
| "I-trigger": "15", | |
| "B-location": "16", | |
| "I-location": "17", | |
| "I-location-city": "18", | |
| "I-organization-company": "19", | |
| "B-duration": "20", | |
| "I-duration": "21", | |
| "I-location-street": "22", | |
| "I-location-stop": "23", | |
| "I-location-route": "24", | |
| "B-person": "25", | |
| "I-date": "26", | |
| "B-set": "27", | |
| "B-money": "28", | |
| "I-person": "29", | |
| "I-money": "30", | |
| "B-distance": "31", | |
| "I-distance": "32", | |
| "I-number": "33", | |
| "B-disaster-type": "34", | |
| "B-org-position": "35", | |
| "I-org-position": "36", | |
| "I-set": "37", | |
| "B-percent": "38", | |
| "I-percent": "39", | |
| "I-disaster-type": "40" | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.26.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 31102 | |
| } | |