lesterpjy / numeric-t5 Goto Github PK

from transformers import T5Tokenizer, TFT5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained('t5-small')
model = TFT5ForConditionalGeneration.from_pretrained('t5-small')
model.compile()
model.load_weights("/content/drive/MyDrive/NT5/Checkpoints/model_chkpt_e_42_vl_0.04173539.h5")

All model checkpoint layers were used when initializing TFT5ForConditionalGeneration.

All the layers of TFT5ForConditionalGeneration were initialized from the model checkpoint at t5-small.
If your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5ForConditionalGeneration for predictions without further training.
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-5-afea930cf1a9> in <module>()
      4 model = TFT5ForConditionalGeneration.from_pretrained('t5-small')
      5 #model.compile()
----> 6 model.load_weights("/content/drive/MyDrive/NT5/Checkpoints/model_chkpt_e_42_vl_0.04173539.h5")

1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py in load_weights(self, filepath, by_name, skip_mismatch, options)
   2232             f, self.layers, skip_mismatch=skip_mismatch)
   2233       else:
-> 2234         hdf5_format.load_weights_from_hdf5_group(f, self.layers)
   2235 
   2236   def _updated_config(self):

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/saving/hdf5_format.py in load_weights_from_hdf5_group(f, layers)
    686                      'containing ' + str(len(layer_names)) +
    687                      ' layers into a model with ' + str(len(filtered_layers)) +
--> 688                      ' layers.')
    689 
    690   # We batch weight value assignments in a single backend call

ValueError: You are trying to load a weight file containing 4 layers into a model with 3 layers.