"After training, we can save the weights of TransformerEncoder for the downstream fine-tuning tasks. Please see [run_pretraining.py](https://github.com/tensorflow/models/blob/master/official/nlp/bert/run_pretraining.py) for the full example.\n"
"After training, we can save the weights of TransformerEncoder for the downstream fine-tuning tasks. Please see [run_pretraining.py](https://github.com/tensorflow/models/blob/master/official/legacy/bert/run_pretraining.py) for the full example.\n"
]
},
{
...
...
@@ -426,7 +426,7 @@
"id": "Zdf03YtZmd_d"
},
"source": [
"With the `loss`, you can optimize the model. Please see [run_squad.py](https://github.com/tensorflow/models/blob/master/official/nlp/bert/run_squad.py) for the full example."
"With the `loss`, you can optimize the model. Please see [run_squad.py](https://github.com/tensorflow/models/blob/master/official/legacy/bert/run_squad.py) for the full example."
]
},
{
...
...
@@ -538,7 +538,7 @@
"id": "mzBqOylZo3og"
},
"source": [
"With the `loss`, you can optimize the model. Please see [run_classifier.py](https://github.com/tensorflow/models/blob/master/official/nlp/bert/run_classifier.py) or the [Fine tune_bert](https://www.tensorflow.org/text/tutorials/fine_tune_bert) notebook for the full example."
"With the `loss`, you can optimize the model. Please see [run_classifier.py](https://github.com/tensorflow/models/blob/master/official/legacy/bert/run_classifier.py) or the [Fine tune_bert](https://www.tensorflow.org/text/tutorials/fine_tune_bert) notebook for the full example."
