I want to use triplet loss to fine tune my Bert model, so I define a customize Trainer like that:

```
class TripletLossTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False):
anchor_data, positive_data, negative_data = inputs.values()
anchor_output = self.use_avg_2(anchor_data, model)
positive_output = self.use_avg_2(positive_data, model)
negative_output = self.use_avg_2(negative_data, model)
triplet_loss = nn.TripletMarginWithDistanceLoss(distance_function=lambda x, y: 1.0 -
F.cosine_similarity(x, y),margin=0.5)
# compute custom loss
loss = triplet_loss(anchor_output, positive_output, negative_output)
return loss
```

where anchor_data, positive_data, negative_data are tokenized result with type <class âtransformers.tokenization_utils_base.BatchEncodingâ>, and the use _avg_2 function is for computing the average value for the last hidden state for each data with a Bert model.

When I do the training process, it will keep reporting warning like

Could not estimate the number of tokens of the input, floating-point operations will not be computed

Could not estimate the number of tokens of the input, floating-point operations will not be computed

Could not estimate the number of tokens of the input, floating-point operations will not be computed

I wonder what does the floating-point operations mean? will it influence the performance of my training? Thanks!