TypeError: string indices must be integers

I’m getting this error while trying to implement the project Valuenet (GitHub - brunnurs/valuenet: ValueNet: A Neural Text-to-SQL Architecture Incorporating Values) on my laptop and I don’t understand what exactly it means.

I’m getting this error after I try to train the Model using:- python src/main.py

Configuration:- Windows 10 home, Nvidia gtx 1650 with cuda enabled.

Output of wandb log file:-
*** parsed configuration from command line and combine with constants ***
argument: exp_name=exp
argument: seed=90
argument: toy=False
argument: data_set=spider
argument: batch_size=1
argument: cuda=False
argument: encoder_pretrained_model=bert-base-uncased
argument: max_seq_length=512
argument: num_epochs=5.0
argument: lr_base=0.001
argument: lr_connection=0.0001
argument: lr_transformer=2e-05
argument: scheduler_gamma=0.5
argument: max_grad_norm=1.0
argument: clip_grad=5.0
argument: loss_epoch_threshold=50
argument: sketch_loss_weight=1.0
argument: column_pointer=True
argument: embed_size=300
argument: hidden_size=300
argument: action_embed_size=128
argument: att_vec_size=300
argument: type_embed_size=128
argument: col_embed_size=300
argument: readout=identity
argument: column_att=affine
argument: dropout=0.3
argument: beam_size=5
argument: decode_max_time_step=40
argument: data_dir=data\spider
argument: model_output_dir=experiments
Run experiment ‘exp__20210517_150422’
We use the device: ‘cpu’ and 0 gpu’s.
Loading from datasets…
Load data from data\spider\original\tables.json. N=166
Load data from data\spider\train.json. N=7000
Load data from data\spider\dev.json. N=1032
Successfully loaded pre-trained transformer ‘bert-base-uncased’
Use Column Pointer: True
Build optimizer and scheduler. Total training steps: 35000.0
Start training with 5.0 epochs

0%| | 0/5 [00:00<?, ?it/s]

Training: 0%| | 0/7000 [00:00<?, ?it/s]
Training: 0%| | 0/7000 [00:00<?, ?it/s]

0%| | 0/5 [00:00
sketch_loss_weight=sketch_loss_weight)
File “E:\MTECH SUBJECTS\Project\Implementation\valuenet1\valuenet\src\training.py”, line 32, in train
sketch_loss, lf_loss = model.forward(examples)
File “E:\MTECH SUBJECTS\Project\Implementation\valuenet1\valuenet\src\model\model.py”, line 117, in forward
batch.values)
File “D:\Python\Anaconda3\envs\valuenet\lib\site-packages\torch\nn\modules\module.py”, line 889, in _call_impl
result = self.forward(*input, **kwargs)
File “E:\MTECH SUBJECTS\Project\Implementation\valuenet1\valuenet\src\model\encoder\encoder.py”, line 65, in forward
averaged_hidden_states_question, pointers_after_question = self._average_hidden_states_question(last_hidden_states, all_question_span_lengths)
File “E:\MTECH SUBJECTS\Project\Implementation\valuenet1\valuenet\src\model\encoder\encoder.py”, line 141, in _average_hidden_states_question
averaged_span = torch.mean(last_hidden_states[batch_itr_idx, pointer: pointer + span_length, :],
TypeError: string indices must be integers

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