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
Update config.json
Browse files- config.json +13 -0
config.json
ADDED
|
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"attention_probs_dropout_prob": 0.1,
|
| 3 |
+
"hidden_act": "gelu",
|
| 4 |
+
"hidden_dropout_prob": 0.1,
|
| 5 |
+
"hidden_size": 768,
|
| 6 |
+
"initializer_range": 0.02,
|
| 7 |
+
"intermediate_size": 3072,
|
| 8 |
+
"max_position_embeddings": 512,
|
| 9 |
+
"num_attention_heads": 12,
|
| 10 |
+
"num_hidden_layers": 12,
|
| 11 |
+
"type_vocab_size": 2,
|
| 12 |
+
"vocab_size": 31102
|
| 13 |
+
}
|