hetpandya / paraphrase-datasets-pretrained-models Goto Github PK

/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:3365: FutureWarning: 
`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of HuggingFace Transformers. Use the regular
`__call__` method to prepare your inputs and the tokenizer under the `as_target_tokenizer` context manager to prepare
your targets.

Here is a short example:

model_inputs = tokenizer(src_texts, ...)
with tokenizer.as_target_tokenizer():
    labels = tokenizer(tgt_texts, ...)
model_inputs["labels"] = labels["input_ids"]

See the documentation of your specific tokenizer for more details on the specific arguments to the tokenizer of choice.
For a more complete example, see the implementation of `prepare_seq2seq_batch`.

  warnings.warn(formatted_warning, FutureWarning)
Using Adafactor for T5
Epoch 1 of 1: 100%
1/1 [4:37:28<00:00, 16648.54s/it]
Epochs 0/1. Running Loss: nan: 100%
9347/9347 [4:37:23<00:00, 1.38s/it]
/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
  "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)
WARNING:root:NaN or Inf found in input tensor.
WARNING:root:NaN or Inf found in input tensor.
WARNING:root:NaN or Inf found in input tensor.
WARNING:root:NaN or Inf found in input tensor.
WARNING:root:NaN or Inf found in input tensor.
WARNING:root:NaN or Inf found in input tensor.
WARNING:root:NaN or Inf found in input tensor.
WARNING:root:NaN or Inf found in input tensor.
WARNING:root:NaN or Inf found in input tensor.
WARNING:root:NaN or Inf found in input tensor.
WARNING:root:NaN or Inf found in input tensor.
(584, nan)

Generating outputs: 100%
1/1 [00:00<00:00, 1.60it/s]
Decoding outputs: 100%
5/5 [00:01<00:00, 1.65s/it]
[['<extra_id_0>.',
  '<extra_id_0>.',
  '<extra_id_0>',
  '<extra_id_0>) <extra_id_36> ein.',
  '<extra_id_0> waren']]