Instructions to use kbnlresearch/nl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kbnlresearch/nl2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kbnlresearch/nl2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kbnlresearch/nl2") model = AutoModelForMaskedLM.from_pretrained("kbnlresearch/nl2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_config.json from kbnlresearch/nl2: direct link, hf CLI and curl.
- Browser
- Download file 395 Bytes
-
https://huggingface.co/kbnlresearch/nl2/resolve/main/model_config.json
- Command line
-
hf download hf://kbnlresearch/nl2/model_config.json
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curl -L -o model_config.json https://huggingface.co/kbnlresearch/nl2/resolve/main/model_config.json
395 Bytes
| {"bert_model": "bert-base-multilingual-cased", "do_lower": false, "max_seq_length": 180, "num_labels": 20, "label_map": {"1": "O", "2": "B-PER", "3": "I-PER", "4": "B-ORG", "5": "I-ORG", "6": "B-LOC", "7": "I-LOC", "8": "B-DAT", "9": "I-DAT", "10": "B-REF", "11": "I-REF", "12": "B-DOM", "13": "I-DOM", "14": "B-QUA", "15": "I-QUA", "16": "B-MISC", "17": "I-MISC", "18": "[CLS]", "19": "[SEP]"}} |