Instructions to use kd13/RoPERT-MLM-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kd13/RoPERT-MLM-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kd13/RoPERT-MLM-mini", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("kd13/RoPERT-MLM-mini", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 688 Bytes
68b16ff 7d65022 68b16ff | 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": [
"MyBertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"auto_map": {
"AutoConfig": "configuration_mybert.MyBertConfig",
"AutoModelForMaskedLM": "modeling_mybert.MyBertForMaskedLM",
"AutoModel": "modeling_mybert.MyBertModel"
},
"dtype": "float32",
"hidden_dropout_prob": 0.1,
"hidden_size": 384,
"initializer_range": 0.02,
"intermediate_size": 1536,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 128,
"model_type": "mybert",
"num_attention_heads": 8,
"num_hidden_layers": 8,
"pad_token_id": 0,
"rope_theta": 10000.0,
"tie_word_embeddings": true,
"transformers_version": "5.0.0",
"vocab_size": 16839
}
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