Language Model Zoo 🦍


(Sarada Lee) #165

In SP, vocab_size is the number of token. I am a bit confused with the maximum tokens. Please clarify.


(Thomas) #166

As you say, the 30.000 is the vocab_size (= number of unique tokens). The 100 million tokens are the total corpus size, so “number of words in Wikipedia” if you use wikipedia and word segmentation. So for very large Wikipedias, you can just take the nicest articles.


(Sarada Lee) #167

Sorry, I misunderstood your question.

After downloaded the wiki dataset, I only did the following “minimal preprocessing”:
1 put the dataset into a dataframe and removed all columns, except for ‘text’;
2 get rid of sub-headings inside the text column;
3 save the dataframe (without header and index) into a csv file.

I tried to join all sentences into one long .txt file. However, SP does not like it.


(Thomas) #168

Say, what is the vision for the Language Model Zoo here, do you have a preference between SentencePiece-Tokenized and Spacy-tokenized models?


(Jeremy Howard) #169

We should use whatever works best for each language. I expect that means sentence-piece for non-segmented languages like Chinese and Korean, and for agglomerative languages like Finnish, and spacy for everything else.


(Jeremy Howard) #170

You definitely want the smaller vocab size. There’s no way to learn a generalizable embedding of such a long and rare sequence. Embeddings handle multiple meanings just fine, as long as they’re part of a non-linear function (like a neural net).


(Sooheon Kim) #171

Got it. Effectively this gives us semantic paragraphs as sentence inputs to sentencepiece.


(Thomas) #172

My interpretation of that requirement is that otherwise, sentencepiece might be inclined to merge characters from different sentences, which would make no sense. (E.g. if many english sentences start with "A " and end with a full stop and space, you would not want sentencepiece to make “. A” a token in the vocab.
Probably batching also uses this implicitly, if you feed too much data, sentencepiece blows up the memory quite surely.
I would be interested in your intuition on this, too.


(Thomas) #173

While training my first round of German language model:

Is there a suggested base training procedure from which to start and depart? I have now taken train_lm from
imdb_scripts/tain_tri_wt.py based on a comment in another thread. Edit So I just saw that binga’s notebookhas a comment about learning from scratch, I was confused by the PRE_PATH referring to the english wt103, which I would not expect to use.

(I changed sampled_sm because the LanguageModelData liked md.n_tok better than md.nt).

Is that approximately OK?

@rother Do you have a particular German tweet dataset in mind? There seem to be several published last year. Could you add a link, please? I’ll add links to the ones I found.


(Monique Monteiro) #174

Hi all,

I’m having an issue with the following line in Paperspace GPU:

learner.fit(lrs, 1, wds=wd, use_clr=(20,10), cycle_len=15)

After completing the first epoch (there should be 15), the process halts with almost 100% CPU time:

(fastai) paperspace@psdtzkq1p:~/fastai/courses/dl2$ ps -eo pid,ppid,cmd,%mem,%cpu --sort=-%mem | head
PID PPID CMD %MEM %CPU
1897 1 python PT_Language_Model_co 17.3 99.8

Does anyone have similar problem or has any idea about what may be happening? After more than 4 hours of training I’ll have to interrupt the process because it doesn’t seem it will come back.


(Monique Monteiro) #176

fast.ai only accepts English tokenizer. I tried to use the following code:

Tokenizer(lang=‘pt’)

But the following error occurs:

OSError: [E050] Can’t find model ‘en’. It doesn’t seem to be a shortcut link, a Python package or a valid path to a data directory.


(Thomas) #177

You need to install the Portuguese language model for spacy as described in: https://spacy.io/models/

Best regards

Thomas


(Monique Monteiro) #178

I installed it successfully. Even so fast.ai doesn’t recognize ‘pt’.


(Thomas) #179

It sounds like a spacy error. Does

import spacy
nlp = spacy.load("pt")

work as expected?
Spacy tries to play a clever game with symlinks (which seems to have potential to go wrong). In the directory where spacy is installed (seen with print(spacy)) there is a data diirectory. There spacy expects symlinks to the actual model data.
For example, for me en is a symlink to ../../en_core_web_sm.
You would have to have something there for pt.

Alternatively, you can pass the full name as lang to Tokenizer.

Best regards

Thomas


(Kristian Rother) #180

I added some resources and will add new ones as soon as I find them.


(Piotr Czapla) #181

Have anyone came across Stanford Sentiment Treebank and know how to work with it? (https://nlp.stanford.edu/sentiment/treebank.html from 2013).

The latest (2017) sentiment benchmark for polish follows the idea and requires models to estimate not only the sentiment of an entire sentence but all sentiments of subsentences as well. I’m trying to figure out the best way forward to compare polish STOA with a universal language model. Are sentiment treebanks used nowadays?

If my hunch is correct and the Treebank is an old idea does anyone knows about a paper that explains why so that I can talk with the organizers of poleval to maybe get different scoring?
Or if I’m wrong and the Treebanks are still useful does anyone know how to best change our Universal Language Model to output sentiment for a treebank?


(Monique Monteiro) #182

Hi Thomas,

There are no sym links. I had tested previously in Google Colab, but now in Paperspace, the following output is generated:

0

Warning: no model found for 'pt'

**Only loading the 'pt' tokenizer.**


Warning: no model found for 'en'

**Only loading the 'en' tokenizer.**


Warning: no model found for 'en'

Only loading the 'en' tokenizer.


Warning: no model found for 'en'


Only loading the 'en' tokenizer.

Warning: no model found for 'en'

Only loading the 'en' tokenizer.

BrokenProcessPool Traceback (most recent call last)
in ()
----> 1 tok_trn, trn_labels = get_all(df_trn, 1)
2 tok_val, val_labels = get_all(df_val, 1)

in get_all(df, n_lbls)
14 print(i)
15 #pdb.set_trace()
—> 16 tok_, labels_ = get_texts(r, n_lbls)
17 tok += tok_;
18 labels += labels_

in get_texts(df, n_lbls)
5 #texts = texts.apply(fixup).values.astype(str)
6
----> 7 tok = Tokenizer(‘pt’).proc_all_mp(partition_by_cores(texts)) # splits the list into sublists for processing by each core
8 # Lower and upper case is inside the tokenizer
9 return tok, list(labels)

~/fastai/courses/dl2/fastai/text.py in proc_all_mp(ss, lang)
99 ncpus = num_cpus()//2
100 with ProcessPoolExecutor(ncpus) as e:
–> 101 return sum(e.map(Tokenizer.proc_all, ss, [lang]*len(ss)), [])
102
103

~/anaconda3/envs/fastai/lib/python3.6/concurrent/futures/process.py in _chain_from_iterable_of_lists(iterable)
364 careful not to keep references to yielded objects.
365 “”"
–> 366 for element in iterable:
367 element.reverse()
368 while element:

~/anaconda3/envs/fastai/lib/python3.6/concurrent/futures/_base.py in result_iterator()
584 # Careful not to keep a reference to the popped future
585 if timeout is None:
–> 586 yield fs.pop().result()
587 else:
588 yield fs.pop().result(end_time - time.time())

~/anaconda3/envs/fastai/lib/python3.6/concurrent/futures/_base.py in result(self, timeout)
430 raise CancelledError()
431 elif self._state == FINISHED:
–> 432 return self.__get_result()
433 else:
434 raise TimeoutError()

~/anaconda3/envs/fastai/lib/python3.6/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
–> 384 raise self._exception
385 else:
386 return self._result

BrokenProcessPool: A process in the process pool was terminated abruptly while the future was running or pending.


(Thomas) #183

Hi Monique,

the long names for the language models correspond to modules in the usual python search path. Can you see your language model there / with pip list?
Or you could just use the long name of the module you installed with (i.e. Tokenizer(lang='pt_core_news_sm')).

Best regards

Thomas


(Thomas) #184

Hi Sylvain,

using your parameters for training a German LM, I got to 3.84264 3.76195 0.315587 after 5 epochs, which is better than what I had with the “default policy” at that time (the default was 4.242864 4.056991 0.320959 after 7 epochs).
Unfortunately then my GPU seemed to show a defect, and it’ll be a while until I have a replacement :cry:.
But the high rates are awesome, thank you for sharing!

Best regards

Thomas


(Monique Monteiro) #185

Hi Thomas,

Strangely, the package doesn’t appear by typing pip list. Did you manage to use the fast.ai Tokenizer class with any language other than English (German?). Luckily, the EN tokenizer does a good job for Portuguese.

Best regards,
Monique