Instructions to use kd13/RoPERT-MLM-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kd13/RoPERT-MLM-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kd13/RoPERT-MLM-small", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("kd13/RoPERT-MLM-small", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "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": 512, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2048, | |
| "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 | |
| } | |