Instructions to use kerasformers/deberta_v3_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use kerasformers/deberta_v3_base with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://kerasformers/deberta_v3_base") - KerasFormers
How to use kerasformers/deberta_v3_base with KerasFormers:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 664 Bytes
e1ae4d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"library_name": "kerasformers",
"kerasformers_version": "1.1.3",
"model_module": "kerasformers.models.deberta_v3",
"model_class": "DebertaV3Model",
"variant": "deberta_v3_base",
"weights": "model.weights.h5",
"model_type": "deberta_v3",
"vocab_size": 128100,
"embed_dim": 768,
"num_layers": 12,
"num_heads": 12,
"mlp_dim": 3072,
"max_position_embeddings": 512,
"max_relative_positions": 512,
"position_buckets": 256,
"pos_att_type": [
"p2c",
"c2p"
],
"norm_rel_ebd": true,
"conv_kernel_size": 0,
"conv_act": "gelu",
"hidden_act": "gelu",
"layer_norm_eps": 1e-07,
"pad_token_id": 0
} |