'ValueError: You have to specify either input_ids or inputs_embeds'

{'hypothesis': 'Product and geography are what make cream skimming work. ',
, 'idx': 0,
, 'label': 1,
, 'premise': 'Conceptually cream skimming has two basic dimensions - product and geography.'}

'ValueError: expected sequence of length 43 at dim 1 (got 37)'

'[CLS] conceptually cream skimming has two basic dimensions - product and geography. [SEP] product and geography are what make cream skimming work. [SEP]'

dict_keys(['attention_mask', 'hypothesis', 'idx', 'input_ids', 'label', 'premise'])

transformers.models.distilbert.modeling_distilbert.DistilBertForSequenceClassification

[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

True

1

['entailment', 'neutral', 'contradiction']

~/git/transformers/src/transformers/data/data_collator.py in torch_default_data_collator(features)
,    105                 batch[k] = torch.stack([f[k] for f in features])
,    106             else:
,--> 107                 batch[k] = torch.tensor([f[k] for f in features])
,    108 
,    109     return batch
,
,ValueError: expected sequence of length 45 at dim 1 (got 76)

<function transformers.data.data_collator.default_data_collator(features: List[InputDataClass], return_tensors='pt') -> Dict[str, Any]>

RuntimeError: CUDA error: CUBLAS_STATUS_ALLOC_FAILED when calling `cublasCreate(handle)`

~/.pyenv/versions/3.7.9/envs/base/lib/python3.7/site-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
,   2386         )
,   2387     if dim == 2:
,-> 2388         ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
,   2389     elif dim == 4:
,   2390         ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
,
,IndexError: Target 2 is out of bounds.

2

TypeError: only size-1 arrays can be converted to Python scalars

TypeError: only size-1 arrays can be converted to Python scalars

TypeError: only size-1 arrays can be converted to Python scalars

((8, 3), (8,))

{'accuracy': 0.625}

{'accuracy': 1.0}