Hi,
I’ve wrote a method called show_top_losses() to enhance TextClassificationInterpretation inspired by plot_top_losses in vision.learn.
This method can creates a tabulation showing the first k texts in top_losses along with their prediction, actual, loss, and probability of actual class.
like this: (on my own dataset)
my code:
def show_top_losses(self, k:int)->None:
table_header = ['Text', 'Prediction', 'Actual', 'Loss', 'Probability']
table_data = []
tl_val,tl_idx = self.top_losses()
for i,idx in enumerate(tl_idx):
tx,cl = self.data.dl(self.ds_type).dataset[idx]
cl = cl.data
classes = self.data.classes
tmp = (self.cut_by_line(tx.text), f'{classes[self.pred_class[idx]]}', f'{classes[cl]}', f'{self.losses[idx]:.2f}', f'{self.probs[idx][cl]:.2f}')
table_data.append(tmp)
k -= 1
if k==0: break
print(tabulate(table_data, headers=table_header, tablefmt='orgtbl'))
def cut_by_line(self,text):
res = ""
width = 80
lines = len(text) // width
if lines == 0:
res += text
else:
for i in range(lines):
res += text[i * width:(i + 1) * width] + '\n'
res += text[(range(lines)[-1] + 1) * width:]
return res
I thought it is useful to me. May I add this to awd_lstm.py？@sgugger