Instructions to use muhtasham/TajBERTo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muhtasham/TajBERTo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="muhtasham/TajBERTo")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("muhtasham/TajBERTo") model = AutoModelForMaskedLM.from_pretrained("muhtasham/TajBERTo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +0 -1
config.json
CHANGED
|
@@ -7,7 +7,6 @@
|
|
| 7 |
"classifier_dropout": null,
|
| 8 |
"eos_token_id": 2,
|
| 9 |
"hidden_act": "gelu",
|
| 10 |
-
"gradient_checkpointing": false,
|
| 11 |
"hidden_dropout_prob": 0.1,
|
| 12 |
"hidden_size": 768,
|
| 13 |
"initializer_range": 0.02,
|
|
|
|
| 7 |
"classifier_dropout": null,
|
| 8 |
"eos_token_id": 2,
|
| 9 |
"hidden_act": "gelu",
|
|
|
|
| 10 |
"hidden_dropout_prob": 0.1,
|
| 11 |
"hidden_size": 768,
|
| 12 |
"initializer_range": 0.02,
|