Fill-Mask
Transformers
Safetensors
English
roformer
binary-analysis
file-type-detection
byte-level
mlm
rope
magic-bytes
security
Eval Results (legacy)
Instructions to use mjbommar/magic-bert-50m-roformer-mlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjbommar/magic-bert-50m-roformer-mlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mjbommar/magic-bert-50m-roformer-mlm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mjbommar/magic-bert-50m-roformer-mlm") model = AutoModelForMaskedLM.from_pretrained("mjbommar/magic-bert-50m-roformer-mlm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 545 Bytes
72cc4e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"architectures": [
"RoFormerForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"embedding_size": 512,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 512,
"initializer_range": 0.02,
"intermediate_size": 2048,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "roformer",
"num_attention_heads": 8,
"num_hidden_layers": 8,
"pad_token_id": 2,
"rotary_value": false,
"transformers_version": "4.57.3",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 32768
} |