Instructions to use NCAI/Bert_backup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NCAI/Bert_backup with Transformers:
# Load model directly from transformers import LeanAlbertForPretraining model = LeanAlbertForPretraining.from_pretrained("NCAI/Bert_backup", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "LeanAlbertForPretraining", "LeanAlbertForTokenClassification", "LeanAlbertForSequenceClassification" | |
| ], | |
| "model_type": "lean_albert", | |
| "num_hidden_layers": 32, | |
| "num_hidden_groups": 32, | |
| "num_inner_groups": 1, | |
| "share_large_matrices": true, | |
| "adapter_dim": 32, | |
| "hidden_size": 2560, | |
| "intermediate_size": 10240, | |
| "embedding_size": 256, | |
| "num_attention_heads": 64, | |
| "vocab_size": 999, | |
| "hidden_act": "gelu_new", | |
| "hidden_act_gated": true, | |
| "sandwich_norm": true, | |
| "inner_group_num": 1, | |
| "position_embedding_type": "rotary", | |
| "hidden_dropout_prob": 0, | |
| "classifier_dropout_prob": 0.1, | |
| "attention_probs_dropout_prob": 0, | |
| "layer_norm_eps": 1e-12, | |
| "type_vocab_size": 2, | |
| "pad_token_id": 0, | |
| "bos_token_id": 2, | |
| "eos_token_id": 3 | |
| } | |