Instructions to use kamalkraj/deberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kamalkraj/deberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="kamalkraj/deberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kamalkraj/deberta-base") model = AutoModel.from_pretrained("kamalkraj/deberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from kamalkraj/deberta-base: direct link, hf CLI and curl.
- Browser
- Download file 744 Bytes
-
https://huggingface.co/kamalkraj/deberta-base/resolve/main/config.json
- Command line
-
hf download hf://kamalkraj/deberta-base/config.json
-
curl -L -o config.json https://huggingface.co/kamalkraj/deberta-base/resolve/main/config.json
744 Bytes
| { | |
| "_name_or_path": "microsoft/deberta-base", | |
| "architectures": [ | |
| "DebertaModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-07, | |
| "max_position_embeddings": 512, | |
| "max_relative_positions": -1, | |
| "model_type": "deberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "pooler_dropout": 0, | |
| "pooler_hidden_act": "gelu", | |
| "pooler_hidden_size": 768, | |
| "pos_att_type": [ | |
| "c2p", | |
| "p2c" | |
| ], | |
| "position_biased_input": false, | |
| "relative_attention": true, | |
| "transformers_version": "4.10.0.dev0", | |
| "type_vocab_size": 0, | |
| "vocab_size": 50265 | |
| } | |