After loading a pretrained language model or fine-tuning a language model on the target data, I don’t know how to get the language model predictions and associated probabilities on a validation set

```
LM_PATH=Path('data/imdb_lm/')
tok_trn = np.load(LM_PATH/'tmp'/'tok_trn.npy')
tok_val = np.load(LM_PATH/'tmp'/'tok_val.npy')
trn_lm = np.array([[stoi[o] for o in p] for p in tok_trn])
val_lm = np.array([[stoi[o] for o in p] for p in tok_val])
# Take just 10 examples for testing purposes
trn_lm = trn_lm[:5]
val_lm = val_lm[:5]
trn_dl = LanguageModelLoader(np.concatenate(trn_lm), bs, bptt)
val_dl = LanguageModelLoader(np.concatenate(val_lm), bs, bptt)
md = LanguageModelData(PATH, 1, vs, trn_dl, val_dl, bs=bs, bptt=bptt)
drops = np.array([0.25, 0.1, 0.2, 0.02, 0.15])*0.7
learn = md.get_model(opt_fn, em_sz, nh, nl,
dropouti=drops[0], dropout=drops[1], wdrop=drops[2], dropoute=drops[3], dropouth=drops[4])
learn.metrics = [accuracy]
# Load the language model pre-trained on WikiText103
learn.load('bwd_wt103.h5')
learn.data.test_dl = val_dl
preds = learn.predict(is_test=True)
```

Then, I don’t know how to show the language model predictions and probabilities for an example (a chunk of text, bptt) in the val_dl. Anyone knows how to get them? Thanks in advance.