Instructions to use deepset/gbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/gbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="deepset/gbert-base")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("deepset/gbert-base", dtype="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 362 Bytes
b8185d8 f62eb75 b8185d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"max_position_embeddings": 512,
"num_attention_heads": 12,
"num_hidden_layers": 12,
"type_vocab_size": 2,
"vocab_size": 31102
}
|