Instructions to use magicslabnu/OutEffHop_bert_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use magicslabnu/OutEffHop_bert_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="magicslabnu/OutEffHop_bert_base", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("magicslabnu/OutEffHop_bert_base", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("magicslabnu/OutEffHop_bert_base", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Update configuration_bert.py
Browse files- configuration_bert.py +1 -1
configuration_bert.py
CHANGED
|
@@ -115,7 +115,7 @@ class BertConfig(PretrainedConfig):
|
|
| 115 |
pad_token_id=0,
|
| 116 |
position_embedding_type="absolute",
|
| 117 |
use_cache=True,
|
| 118 |
-
classifier_dropout=None
|
| 119 |
**kwargs,
|
| 120 |
):
|
| 121 |
super().__init__(pad_token_id=pad_token_id, **kwargs)
|
|
|
|
| 115 |
pad_token_id=0,
|
| 116 |
position_embedding_type="absolute",
|
| 117 |
use_cache=True,
|
| 118 |
+
classifier_dropout=None,
|
| 119 |
**kwargs,
|
| 120 |
):
|
| 121 |
super().__init__(pad_token_id=pad_token_id, **kwargs)
|