Instructions to use chitanda/merit-deberta-v2-xlarge-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chitanda/merit-deberta-v2-xlarge-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, DebertaV2ForMultipleChoicePreTrain tokenizer = AutoTokenizer.from_pretrained("chitanda/merit-deberta-v2-xlarge-v1") model = DebertaV2ForMultipleChoicePreTrain.from_pretrained("chitanda/merit-deberta-v2-xlarge-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "pretrained-models/deberta-v2-xlarge", | |
| "add_enhanced_decoder": true, | |
| "architectures": [ | |
| "DebertaV2ForMultipleChoicePreTrain" | |
| ], | |
| "attention_head_size": 64, | |
| "attention_probs_dropout_prob": 0.1, | |
| "conv_act": "gelu", | |
| "conv_kernel_size": 3, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1536, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 6144, | |
| "layer_norm_eps": 1e-07, | |
| "max_position_embeddings": 512, | |
| "max_relative_positions": -1, | |
| "mlp_hidden_size": 3072, | |
| "model_type": "deberta-v2", | |
| "norm_rel_ebd": "layer_norm", | |
| "num_attention_heads": 24, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "pooler_dropout": 0, | |
| "pooler_hidden_act": "gelu", | |
| "pooler_hidden_size": 1536, | |
| "pos_att_type": [ | |
| "p2c", | |
| "c2p" | |
| ], | |
| "position_biased_input": false, | |
| "position_buckets": 256, | |
| "relative_attention": true, | |
| "share_att_key": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.15.0", | |
| "type_vocab_size": 0, | |
| "use_stable_embedding": false, | |
| "vocab_size": 128100 | |
| } | |