Training custom classifier

[FOX111]

Hello, I've seen your code at the front page for training a language model

`from fastai.text import *
import multifit

exp = multifit.from_pretrained("name of the model")
fa_config =  exp.pretrain_lm.tokenizer.get_fastai_config(add_open_file_processor=True)
data_lm = (TextList.from_folder(imdb_path, **fa_config)
            .filter_by_folder(include=['train', 'test', 'unsup']) 
            .split_by_rand_pct(0.1)
            .label_for_lm()           
            .databunch(bs=bs))
learn = exp.finetune_lm.get_learner(data_lm)  
# learn is a preconfigured fastai learner with a pretrained model loaded
learn.fit_one_cycle(10)
learn.save_encoder("enc")
...`

I would like to ask how I can then train my own classifier on top of this model, since all guidlines described here https://docs.fast.ai/text.html assume AWD-LSTM architecture, so they will not work with MULTIFIT language model as an encoder.

Thanks

[ghost]

Hi @FOX111, did you manage to solve it or find any workaround?