Instructions to use PaddleCI/tiny-random-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- paddlenlp
How to use PaddleCI/tiny-random-bert with paddlenlp:
from paddlenlp.transformers import AutoTokenizer, BertForMaskedLM tokenizer = AutoTokenizer.from_pretrained("PaddleCI/tiny-random-bert", from_hf_hub=True) model = BertForMaskedLM.from_pretrained("PaddleCI/tiny-random-bert", from_hf_hub=True) - Notebooks
- Google Colab
- Kaggle
File size: 516 Bytes
e490170 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"dtype": "float32",
"fuse": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 8,
"initializer_range": 0.02,
"intermediate_size": 8,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 2,
"num_hidden_layers": 2,
"pad_token_id": 0,
"paddlenlp_version": null,
"pool_act": "tanh",
"type_vocab_size": 2,
"vocab_size": 30522
}
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