Instructions to use neurocode/Icelandic-NER-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neurocode/Icelandic-NER-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="neurocode/Icelandic-NER-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("neurocode/Icelandic-NER-base") model = AutoModelForTokenClassification.from_pretrained("neurocode/Icelandic-NER-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from neurocode/Icelandic-NER-base: direct link, hf CLI and curl.
- Browser
- Download file 1.36 kB
-
https://huggingface.co/neurocode/Icelandic-NER-base/resolve/refs%2Fpr%2F1/config.json
- Command line
-
hf download hf://neurocode/Icelandic-NER-base@refs/pr/1/config.json
-
curl -L -o config.json https://huggingface.co/neurocode/Icelandic-NER-base/resolve/refs%2Fpr%2F1/config.json
1.36 kB
| { | |
| "architectures": [ | |
| "XLMRobertaForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "eos_token_id": 2, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "B-Date", | |
| "1": "B-Location", | |
| "2": "B-Miscellaneous", | |
| "3": "B-Money", | |
| "4": "B-Organization", | |
| "5": "B-Percent", | |
| "6": "B-Person", | |
| "7": "B-Time", | |
| "8": "I-Date", | |
| "9": "I-Location", | |
| "10": "I-Miscellaneous", | |
| "11": "I-Money", | |
| "12": "I-Organization", | |
| "13": "I-Percent", | |
| "14": "I-Person", | |
| "15": "I-Time", | |
| "16": "O" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B-Date": 0, | |
| "B-Location": 1, | |
| "B-Miscellaneous": 2, | |
| "B-Money": 3, | |
| "B-Organization": 4, | |
| "B-Percent": 5, | |
| "B-Person": 6, | |
| "B-Time": 7, | |
| "I-Date": 8, | |
| "I-Location": 9, | |
| "I-Miscellaneous": 10, | |
| "I-Money": 11, | |
| "I-Organization": 12, | |
| "I-Percent": 13, | |
| "I-Person": 14, | |
| "I-Time": 15, | |
| "O": 16 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "total_flos": 122768986395843072, | |
| "type_vocab_size": 1, | |
| "vocab_size": 250002 | |
| } | |