ValueError: Unable to create dataset (name already exists)
Hello, @Alwaysadil!
Could you please have a look at this [gist](https://colab.research.google.com/gist/sushreebarsa/f596ec59abbf469bd97beb1790337bb9/61813.ipynb) which shows a different error?
This error generally occurs when the layer names are duplicates across the namespaces of the pre-trained model and your downstream-task network. It can be useful to choose a unique name to call every layer of your downstream task network. Thank you! Hi @sushreebarsa i resolved that issue by changing .hdf5 to .tf
Thank you for your response
 @Alwaysadil True! It is always recommended to use .tf as  the saving format instead of .hdf5 after TF  v2.9.
Glad it worked for you! 
Could you please confirm if we can close the issue if it is resolved?

Thank you!
 yes @sushreebarsa you can close this issue now 
but please check this below one  i alreaded raised issue  please check #61850
https://github.com/tensorflow/tensorflow/issues/61850
one more that below 

While doing tflite lite model conversion with input signature as string data type i am getting this error
ConverterError: /usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/func_graph.py:670:0: error: 'tf.FastWordpieceTokenizeWithOffsets' op is neither a custom op nor a flex op
:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/func_graph.py:670:0: note: Error code: ERROR_NEEDS_CUSTOM_OPS
/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/func_graph.py:670:0: error: 'tf.TFText>FastWordpieceDetokenize' op is neither a custom op nor a flex op
:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/func_graph.py:670:0: note: Error code: ERROR_NEEDS_CUSTOM_OPS
:0: error: failed while converting: 'main':
Some ops in the model are custom ops, See instructions to implement custom ops: https://www.tensorflow.org/lite/guide/ops_custom
Custom ops: FastWordpieceTokenizeWithOffsets, TFText>FastWordpieceDetokenize
Details:
tf.FastWordpieceTokenizeWithOffsets(tensor<?x!tf_type.string>, tensor<241460xui8>) -> (tensor<?x!tf_type.string>, tensor<?xi64>, tensor<?xi64>, tensor<?xi64>, tensor<?xi64>) : {device = "/device:CPU:0"}
tf.TFText>FastWordpieceDetokenize(tensor<40xi32>, tensor<2xi64>, tensor<339052xui8>) -> (tensor<?x!tf_type.string>) : {device = ""}

kindly please check this below colab link
https://colab.research.google.com/drive/14_ajVH4r4NXN6cDw96XJNBtltoueoGfN?usp=sharing @Alwaysadil Thank you for the confirmation.
We will look into the other one. Thank you! Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61813">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61813">No</a>

### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

2.13.0

### Custom code

Yes

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Epoch 1/10
1/1 [==============================] - ETA: 0s - loss: 6.8405 - accuracy: 0.3250
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-57-865e41f45523> in <cell line: 1>()
----> 1 transformer.fit(train_ds, epochs=EPOCHS, validation_data=val_ds,batch_size=BATCH_SIZE,callbacks=[early_stop, checkpoint_call, plot_losses])

2 frames
/usr/local/lib/python3.10/dist-packages/h5py/_hl/dataset.py in make_new_dset(parent, shape, dtype, data, name, chunks, compression, shuffle, fletcher32, maxshape, compression_opts, fillvalue, scaleoffset, track_times, external, track_order, dcpl, dapl, efile_prefix, virtual_prefix, allow_unknown_filter, rdcc_nslots, rdcc_nbytes, rdcc_w0)
    161         sid = h5s.create_simple(shape, maxshape)
    162 
--> 163     dset_id = h5d.create(parent.id, name, tid, sid, dcpl=dcpl, dapl=dapl)
    164 
    165     if (data is not None) and (not isinstance(data, Empty)):

h5py/_objects.pyx in h5py._objects.with_phil.wrapper()

h5py/_objects.pyx in h5py._objects.with_phil.wrapper()

h5py/h5d.pyx in h5py.h5d.create()

ValueError: Unable to create dataset (name already exists)


i want to save each epoch as a checkpoint two weeks back back without any any error each checkpoint will save as a checkpoint but suddenly now getting error

### Standalone code to reproduce the issue

```shell
import matplotlib.pyplot as plt

from tensorflow.keras.callbacks import Callback


import os

checkpoint_dir = '/model/checkpoints_m_1'

if not os.path.exists(checkpoint_dir):
    os.makedirs(checkpoint_dir)
from tensorflow.keras.callbacks import EarlyStopping
early_stop = EarlyStopping(monitor='val_loss', patience=3)
# Set up the model checkpoint callback
checkpoint_call = ModelCheckpoint(filepath=checkpoint_dir+"/checkpoint_{epoch}.hdf5",
                                  monitor='val_loss',
                                  save_best_only=True,
                                  save_weights_only=False,
                                  mode='min',
                                  save_freq='epoch')
transformer.fit(train_ds, epochs=EPOCHS, validation_data=val_ds,batch_size=BATCH_SIZE,callbacks=[early_stop, checkpoint_call)
```


### Relevant log output

_No response_