Instructions to use hf-tiny-model-private/tiny-random-NezhaForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-NezhaForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-tiny-model-private/tiny-random-NezhaForMaskedLM")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-NezhaForMaskedLM", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 622 Bytes
2a4c25a | 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 | {
"architectures": [
"NezhaForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 2,
"classifier_dropout": 0.1,
"eos_token_id": 3,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 32,
"initializer_range": 0.02,
"intermediate_size": 37,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 128,
"max_relative_position": 64,
"model_type": "nezha",
"num_attention_heads": 4,
"num_hidden_layers": 5,
"pad_token_id": 0,
"torch_dtype": "float32",
"transformers_version": "4.28.0.dev0",
"type_vocab_size": 16,
"use_cache": true,
"vocab_size": 1124
}
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