gmihaila / ml_things

This is where I put things I find useful that speed up my work with Machine Learning. Ever looked in your old projects to reuse those cool functions you created before? Well, this repo is designed to be a Python Library of functions I created in my previous project that can be reused. I also share some Notebooks Tutorials and Python Code Snippets.

About me 💬

George is a PhD candidate at University of North Texas, in the Department of Computer Science. He received his Bachelors Degree in Electrical Engineering in his home-country, Romania, and he got his Masters Degree in Computer Science in the United States.

He worked three summers for State Farm as a Data Scientist (DS) and Machine Learning (ML) Engineer, and he has more than 4 years of experience in Natural Language Processing (NLP), Computer Vision (CV), Deep Learning (DL) and Reinforcement Learning (RL).

He is currently working towards his doctoral thesis in casual dialog generation with persona.

Competencies: data science, machine learning, deep learning, high performance computing, Tensorflow2.0, PyTorch, Python, R.

Contact 🎣

🦊 GitHub: gmihaila

🌐 Website: gmihaila.github.io

👔 LinkedIn: mihailageorge

📓 Medium: @gmihaila

📬 Email: [email protected]

ml_things's Issues

how to custom config file for Bert?

in your notebook :
https://colab.research.google.com/github/gmihaila/ml_things/blob/master/notebooks/pytorch/pretrain_transformers_pytorch.ipynb#scrollTo=UCLtm5BiXona
get_model_config() method load config from model_path, how to change it to custom config?,
for example
config = BertConfig(
vocab_size=10000,
hidden_size=256,
num_hidden_layers=6,
num_attention_heads=4,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
pad_token_id=0,
position_embedding_type="absolute",
truncation=True,
)

I wanto change the num_hidden_layers. for training a lightly Bert model

RuntimeError: CUDA out of memory.

The fine-tuning worked perfectly on BERT, in other words, all default settings in the finetuning notebook, however, while running the longformer, I get this error:
RuntimeError: CUDA out of memory. Tried to allocate 514.00 MiB (GPU 0; 15.75 GiB total capacity; 14.14 GiB already allocated; 106.81 MiB free; 14.32 GiB reserved in total by PyTorch)
I am using 'allenai/longformer-base-4096 '

I am using Google Colab pro with GPU and high-RAM memory.

Early Stopping

Can you please add examples for earlystopping based on the loss metrics during the training?

using a fine-tuned model for prediction on unlabeled data

Hi,

Thanks for creating these helpers and finetuning tutorials. I've found this repo to be very helpful and appreciate all the work you've put into it :).

One question I have is in regards to the following validation function from your gpt2-finetune-classification notebook. Here is the code snippet:

    # speeding up validation
    with torch.no_grad():
        # Forward pass, calculate logit predictions.
        # This will return the logits rather than the loss because we have
        # not provided labels.
        # token_type_ids is the same as the "segment ids", which 
        # differentiates sentence 1 and 2 in 2-sentence tasks.
        # The documentation for this `model` function is here: 
        # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
        outputs = model(**batch)

        # The call to `model` always returns a tuple, so we need to pull the 
        # loss value out of the tuple along with the logits. We will use logits
        # later to to calculate training accuracy.
        loss, logits = outputs[:2]
        
        # Move logits and labels to CPU
        logits = logits.detach().cpu().numpy()

        # Accumulate the training loss over all of the batches so that we can
        # calculate the average loss at the end. `loss` is a Tensor containing a
        # single value; the `.item()` function just returns the Python value 
        # from the tensor.
        total_loss += loss.item()
        
        # get predicitons to list
        predict_content = logits.argmax(axis=-1).flatten().tolist()

        # update list
        predictions_labels += predict_content

I'm trying to repurpose this to predict on unlabeled data. Although the comments in the code suggest that we have not provided the labels, I am getting errors when feeding in a dictionary with one extra other category (indicating that no class has been assigned):

-> 2264         ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
   2265     elif dim == 4:
   2266         ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
IndexError: Target 13 is out of bounds.

Is there a good way to change this to exclude the labels and label dictionary from being fed into the model, and also to exclude the loss from being calculated? Sorry if there is an obvious solution, it seems to be evading me right now.

Thank you for the help!

Tutorials: Pretraining Transformers

First of all, thanks for writing this notebook—it's been a huge help!

I have an unusual situation, in that I have a small, hand-defined vocabulary for a very specific purpose. For this reason, I've been using the BertWordPieceTokenizer for everything (whether MLM or CLM), and loading it with my fixed vocab file. Is there a way I can do this with your notebook?

Thanks in advance.

pad_array padding value

pad_array can only use 0 for padding. Add functionality to select what value to use for padding

pad token as eos token for gpt2 classification

Hello,

First of all thanks a lot for the tutorials!
I have a question regarding the pad token. In the GPT-2 for classification example, you set padding to be the eos token. Why was that the case? Shouldn't every sequence have one eos token at the end of the sequence to be passed for classification just like [CLS] in BERT?

pretrain_transformer - checkpoints save

machine_learning_things/tutorial_notebooks/pretrain_transformer.ipynb

Is saving too many checkpoints on large data. Use a larger save_steps or set save_steps=-1 to avoid saving any checkpoints.

# process training arguments
training_args = TrainingArguments(output_dir='bert_podcast', do_train=True, 
                                  do_eval=True,
                                  save_steps=-1)

Installation error.

Hello, I tried to re-build your GPT2 classifcation page.

But I found an issue.

Maybe some of your requirement is outdated?

ERROR: Could not find a version that satisfies the requirement matplotlib>=3.4.0 (from ml-things)
ERROR: No matching distribution found for matplotlib>=3.4.0

I got this issue.

Thank you.

pre-train

Hi @gmihaila thanks for this splendid library.
Just have a quick question regarding pre-training from scratch, is it possible using a single system. I don't have much data to pre-train.
Any suggestions.
thanks

Longformer issue

Hi @gmihaila
The fine-tuning notebook works very well, but when I use a sample size of larger than 100 records, the longformer notebook shows strange messages.

this has nothing to do with the ML-Things library fix text(), even if I dont use it, I still get some error, upong reducing the sample size, it works fine,
any suggestions please

Question on XLNet training

Hey, thanks for sharing a comprehensive pre training on huggingface.

I have a few questions on training procedure inside your notebook
https://github.com/gmihaila/ml_things/blob/master/notebooks/pytorch/pretrain_transformers_pytorch.ipynb

When I tried to train on XLNet, it throws an error.

ValueError: This collator requires that sequence lengths be even to create a leakage-free perm_mask. Please see relevant comments in source code for details.

What do you do to prevent this?

I tried to it on tokenizer, but I don't know if it is ok.
Something like this

# this input_ids 5 is <pad token>, since xlnet tokenizer ends with <sep> and <cls>, I put that before <sep> (-2)
# attention_mask and token_type_ids just preserve the previous state
def tokenize_function(examples):
    token_res = tokenizer(examples["text"], truncation=True, max_length=MAX_LENGTH)
    for i, item in enumerate(token_res["input_ids"]):
        if len(item) % 2 != 0:
            token_res["input_ids"][i].insert(-2,5)
            token_res["attention_mask"][i].insert(-2,0)
            token_res["token_type_ids"][i].insert(-2,1)

    return token_res

doubts regarding Bert Innerworking

hi,

      i read your article bert inner working.. it is very informative. i have some questions regarding this article. that is, how to show the final sentiment of the two statements?

'I love cats!',
"He hates pineapple pizza."

question for imbalanced classification

How do I input class weights if my classes are imbalanced, (99%,1%) into the GPT2 sequence classification?

Wrong link in gpt2_finetune_classification.ipynb

The link to the google colab notebook in gpt2_finetune_classification.ipynb does not lead to the right notebook. It currently leads to https://colab.research.google.com/github/gmihaila/ml_things/blob/master/notebooks/pytorch/pretrain_transformers_pytorch.ipynb and should be changed to https://colab.research.google.com/github/gmihaila/ml_things/blob/master/notebooks/pytorch/gpt2_finetune_classification.ipynb

AttributeError: 'BucketIterator' object has no attribute

I’m doing seq2seq machine translation on my own dataset. I have preproceed my dataset using this code.

def tokenize_word(text):
  return nltk.word_tokenize(text)

id = Field(sequential=True, tokenize = tokenize_word, lower=True, init_token="<sos>", eos_token="<eos>")
ti = Field(sequential=True, tokenize = tokenize_word, lower=True, init_token="<sos>", eos_token="<eos>")

fields = {'id': ('i', id), 'ti': ('t', ti)}

train_data = TabularDataset.splits(
    path='/content/drive/MyDrive/Colab Notebooks/Tidore/',
    train = 'id_ti.tsv',
    format='tsv',
    fields=fields
)[0]

id.build_vocab(train_data)
ti.build_vocab(train_data)

print(f"Unique tokens in source (id) vocabulary: {len(id.vocab)}")
print(f"Unique tokens in target (ti) vocabulary: {len(ti.vocab)}")

train_iterator = BucketIterator.splits(
    train_data,
    batch_size = batch_size,
    sort_within_batch = True,
    sort_key = lambda x: len(x.id),
    device = device
)

The output of code above is below:

Unique tokens in source (id) vocabulary: 1425
Unique tokens in target (ti) vocabulary: 1297

The problem comes when i tried to split train_data using BucketIterator.split(). When I want to print the value of train_iterator, It says that it has no attribute 'i', eventough i had declare the fields. Here is my code to print it:

for data in train_iterator:
  print(data.i)

The output of code above is below:

AttributeError                            Traceback (most recent call last)

<ipython-input-9-322cc3aa78d6> in <module>()
      1 for data in train_iterator:
----> 2   print(data.i)

AttributeError: 'BucketIterator' object has no attribute 'i'

When i try just to print data, the result makes me more confuse:

I am very confuse, because i don’t know what key i should use for train iterator. Thank you for your help

Making use of saved model

Hello,

i am using pretrain_transformers_pytorch.ipynb, I have successfully followed all steps, my question is how to reload the pretrain_bert saved model to make us of it?

thank you

QA task

the finetune notebook can be used for QA task also?

How to score new data after fine tuning

I use the notebook to fine tune a custom dataset for a multi class classification task. How do I use the fine tuned model on new data to label. Thanks.

predictions - metrics

Hi @gmihaila
any suggestions on saving the predictions of the model.
Also you are using sklearn.metrics, so I think we can use more metrics from the sklearn.metrics

thanks

Question on InnerWorkings Of Bert

First of all thanks for the great Breakdown of the Huggingface-Bert-Model.
From reading the Annotated Transformer and the Bert-paper it seems they use Vanilla Transformer encoders.
But in the Huggingface implementation there is an extra layer for the selfattention(BertSelfOutput). Is there a specific reason for the additional linear layer?
many thanks in advance

a few questions

@gmihaila
Hi, this is such as amazing notebook (pretrain_transformers_pytorch.ipynb).
This is not an issue just few quick questions for my knowledge:

(i) can I use more evaluation metrics here?
(ii) when using the checkpoint, should it be the last one? or the notebook saving the best one?
(iii) if my dataset has multiple features, I would need to pass all of these pieces of information as one piece during fine-tuning? if I intend to have side information, which part should I change? any thoughts
(iv) if I need to include timestamp, any thoughts? or may be during training, we arrange all data temporally?
(v) How can I use the notebook for multi-class @labels

thanks in advance and for the wonderful work that you share with the community