Instructions to use ltg/norbert2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ltg/norbert2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ltg/norbert2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ltg/norbert2") model = AutoModelForMaskedLM.from_pretrained("ltg/norbert2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from ltg/norbert2: direct link, hf CLI and curl.
- Browser
- Download file 383 Bytes
-
https://huggingface.co/ltg/norbert2/resolve/f22bb47f536f62edfcd86ca9320ade990eafbe22/config.json
- Command line
-
hf download hf://ltg/norbert2@f22bb47f536f62edfcd86ca9320ade990eafbe22/config.json
-
curl -L -o config.json https://huggingface.co/ltg/norbert2/resolve/f22bb47f536f62edfcd86ca9320ade990eafbe22/config.json
383 Bytes
| { | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "type_vocab_size": 2, | |
| "vocab_size": 50104 | |
| } | |