Token Classification
Transformers
Safetensors
Norwegian
Norwegian Bokmål
Norwegian Nynorsk
named-entity-recognition
ner
norwegian
bokmal
nynorsk
norbert
custom_code
Eval Results (legacy)
Instructions to use fransis3/norbert4-small-NorNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fransis3/norbert4-small-NorNER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="fransis3/norbert4-small-NorNER", trust_remote_code=True)# Load model directly from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("fransis3/norbert4-small-NorNER", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "ltg/norbert4-small", | |
| "architectures": [ | |
| "GptBertForTokenClassification" | |
| ], | |
| "attention_dropout": 0.0, | |
| "attn_implementation": "flash_attention_2", | |
| "auto_map": { | |
| "AutoConfig": "ltg/norbert4-small--configuration_gptbert.GptBertConfig", | |
| "AutoModel": "ltg/norbert4-small--modeling_gptbert.GptBertModel", | |
| "AutoModelForCausalLM": "ltg/norbert4-small--modeling_gptbert.GptBertForCausalLM", | |
| "AutoModelForMaskedLM": "ltg/norbert4-small--modeling_gptbert.GptBertForMaskedLM", | |
| "AutoModelForMultipleChoice": "ltg/norbert4-small--modeling_gptbert.GptBertForMultipleChoice", | |
| "AutoModelForQuestionAnswering": "ltg/norbert4-small--modeling_gptbert.GptBertForQuestionAnswering", | |
| "AutoModelForSequenceClassification": "ltg/norbert4-small--modeling_gptbert.GptBertForSequenceClassification", | |
| "AutoModelForTokenClassification": "ltg/norbert4-small--modeling_gptbert.GptBertForTokenClassification" | |
| }, | |
| "bos_token_id": 1, | |
| "classifier_dropout": 0.2, | |
| "deterministic_flash_attn": false, | |
| "embedding_dropout": 0.1, | |
| "eos_token_id": 2, | |
| "global_window_length": 8192, | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 320, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-PER", | |
| "2": "I-PER", | |
| "3": "B-ORG", | |
| "4": "I-ORG", | |
| "5": "B-LOC", | |
| "6": "I-LOC", | |
| "7": "B-GPE_LOC", | |
| "8": "I-GPE_LOC", | |
| "9": "B-GPE_ORG", | |
| "10": "I-GPE_ORG", | |
| "11": "B-PROD", | |
| "12": "I-PROD", | |
| "13": "B-EVT", | |
| "14": "I-EVT", | |
| "15": "B-MISC", | |
| "16": "I-MISC", | |
| "17": "B-DRV", | |
| "18": "I-DRV" | |
| }, | |
| "intermediate_size": 832, | |
| "label2id": { | |
| "B-DRV": 17, | |
| "B-EVT": 13, | |
| "B-GPE_LOC": 7, | |
| "B-GPE_ORG": 9, | |
| "B-LOC": 5, | |
| "B-MISC": 15, | |
| "B-ORG": 3, | |
| "B-PER": 1, | |
| "B-PROD": 11, | |
| "I-DRV": 18, | |
| "I-EVT": 14, | |
| "I-GPE_LOC": 8, | |
| "I-GPE_ORG": 10, | |
| "I-LOC": 6, | |
| "I-MISC": 16, | |
| "I-ORG": 4, | |
| "I-PER": 2, | |
| "I-PROD": 12, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-07, | |
| "local_global_ratio": 4, | |
| "local_window_length": 256, | |
| "mask_token_id": 4, | |
| "max_position_embeddings": 16384, | |
| "model": "norbert4", | |
| "num_attention_heads": 5, | |
| "num_layers": 20, | |
| "pad_token_id": 3, | |
| "query_key_head_size": 64, | |
| "rope_theta": 160000, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.48.0", | |
| "unk_token_id": 0, | |
| "use_cache": false, | |
| "value_head_size": 64, | |
| "vocab_size": 51200 | |
| } | |