Instructions to use AIRI-Institute/gena-lm-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIRI-Institute/gena-lm-bert-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, BertForPretraining tokenizer = AutoTokenizer.from_pretrained("AIRI-Institute/gena-lm-bert-base", trust_remote_code=True) model = BertForPretraining.from_pretrained("AIRI-Institute/gena-lm-bert-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from AIRI-Institute/gena-lm-bert-base: direct link, hf CLI and curl.
- Browser
- Download file 754 Bytes
-
https://huggingface.co/AIRI-Institute/gena-lm-bert-base/resolve/main/config.json
- Command line
-
hf download hf://AIRI-Institute/gena-lm-bert-base/config.json
-
curl -L -o config.json https://huggingface.co/AIRI-Institute/gena-lm-bert-base/resolve/main/config.json
754 Bytes
| { | |
| "architectures": [ | |
| "BertForPretraining" | |
| ], | |
| "auto_map": { | |
| "AutoModel": "modeling_bert.BertForPreTraining" | |
| }, | |
| "attention_probs_dropout_prob": 0.1, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 3, | |
| "pre_layer_norm": true, | |
| "last_layer_norm": false, | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.6.0.dev0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 32000 | |
| } | |