gpt2 | Implement of openai gpt2 | Natural Language Processing library
kandi X-RAY | gpt2 Summary
kandi X-RAY | gpt2 Summary
Implement of openai gpt2
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Top functions reviewed by kandi - BETA
- Tokenize text
- Clean text
- Check if a character is a control character
- Check if a character is whitespace
- Forward computation
- Splits the input tensors
- Layer attention
- Merge the rows of x
- RunFinetuning
- Evaluate the model
- Loads a model from the given config
- Evaluate the given model
- Get the model and tokenizer
- Create an instance from a JSON file
- Create a GPT2 model from pretrained data
- Generate a block for a given token
- Returns a set of pair pairs
- Set special tokens
- Get a dictionary of special tokens
- Convert a list of ids to a string
- Convert ids to tokens
- Retrieve bpeanks from merges_file
- Loads the tokenizer
- Load the model from pretrained data
- Runs the direct evaluation of a trained graph
- Runs the finished training
gpt2 Key Features
gpt2 Examples and Code Snippets
Community Discussions
Trending Discussions on gpt2
QUESTION
I get the reoccuring CUDA out of memory error when using the HuggingFace Transformers library to fine-tune a GPT-2 model and can't seem to solve it, despite my 6 GB GPU capacity, which I thought should be enough for fine-tuning on texts. The error reads as follows:
...ANSWER
Answered 2022-Apr-03 at 09:45- If the memory problems still persist, you could opt for
DistillGPT2, as it has a 33% reduction in the parameters of the network (the forward pass is also twice as fast). Particularly for a small GPU memory like 6GB VRAM, it could be a solution/alternative to your problem. - At the same time, it depends on how you preprocess the data. Indeed,
the model is capable of "receiving" a maximum length of
Ntokens (could be for example512/768) depending on the models you choose. I recently trained a named entity recognition model and the model had a maximum length of768tokens. However, when I manually set the dimension of the padded tokens in my PyTorchDataLoader()to a big number, I also got OOM memory (even on3090 24GB VRAM). As I reduced the dimension of the tokens to a much smaller one (512instead of768for example) the training started to work and I did not get any issues with the lack of memory.
TLDR: Reducing the number of tokens in the preprocessing phase, regardless of the max capacity of the network, can also help to solve your memories problem. Note that reducing the number of tokens to process in a sequence is different from the dimension of a token.
QUESTION
I am trying to load a GPT2 fine tuned model in flask initially. The model is being loaded during the init functions using:
...ANSWER
Answered 2021-Nov-20 at 11:21This issue is found to be occurring only if the framework is run using venv or deployment frameworks like uWSGI or gunicorn. It is resolved when transformers version 4.10.0 is used instead of the latest package.
QUESTION
I would like to use Huggingface Transformers to implement a chatbot. Currently, I have the code shown below. The transformer model already takes into account the history of past user input.
Is there something else (additional code) I have to take into account for building the chatbot?
Second, how can I modify my code to run with TensorFlow instead of PyTorch?
Later on, I also plan to fine-tune the model on other data. I also plan to test different models such as BlenderBot and GPT2. I think to test this different models it should be as easy as replacing the corresponding model in AutoTokenizer.from_pretrained("microsoft/DialoGPT-small") and AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
ANSWER
Answered 2021-Nov-21 at 17:17Here is an example of using the DialoGPT model with Tensorflow:
QUESTION
Goal: set min_length and max_length in Hugging Face Transformers generator query.
I've passed 50, 200 as these parameters. Yet, the length of my outputs are much higher...
There's no runtime failure.
...ANSWER
Answered 2022-Mar-04 at 10:30As explained by Narsil on Hugging Face 🤗 Transformers Git Issue response
Solution:Models, don't ingest the text one character at a time, but one token at a time. There are different algorithms to achieve this but basically "My name is Nicolas" gets transformers into ["my", " name", " is", " nic", "olas"] for instance, and each of those tokens have a number.
So when you are generating tokens, they can contain themselves 1 or more characters (usually several and almost any common word for instance). That's why you are seeing 1015 instead of your expected 200 (the tokens here have an average of 5 chars)
As I resolved...
Rename
min_char_len, max_char_lentomin_tokens, max_tokensand simply reduce their values by a ~1/4 or 1/5.
QUESTION
Not always, but occasionally when running my code this error appears.
At first, I doubted it was a connectivity issue but to do with cashing issue, as discussed on an older Git Issue.
Clearing cache didn't help runtime:
...ANSWER
Answered 2022-Mar-03 at 11:59Since I am working in a conda venv and using Poetry for handling dependencies, I needed to re-install torch - a dependency for Hugging Face 🤗 Transformers.
First, install torch: PyTorch's website lets you chose your exact setup/ specification for install. I my case, the command was
QUESTION
I am retraining the GPT2 language model, and am following this blog :
https://towardsdatascience.com/train-gpt-2-in-your-own-language-fc6ad4d60171
Here, they have trained a network on GPT2, and I am trying to recreate a same. However, my dataset is too large(250Mb), so I want to continue training in intervals. In other words, I want to checkpoint the model training. If there is any help, or a piece of code that I can implement to checkpoint and continue training, it would help a great deal for me. Thank you.
...ANSWER
Answered 2022-Feb-22 at 19:10training_args = TrainingArguments(
output_dir=model_checkpoint,
# other hyper-params
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_set,
eval_dataset=dev_set,
tokenizer=tokenizer
)
trainer.train()
# Save the model to model_dir
trainer.save_model()
def prepare_model(tokenizer, model_name_path):
model = AutoModelForCausalLM.from_pretrained(model_name_path)
model.resize_token_embeddings(len(tokenizer))
return model
# Assume tokenizer is defined, You can simply pass the saved model directory path.
model = prepare_model(tokenizer, model_checkpoint)
QUESTION
I am trying to train a dialog system using GPT2. For tokenization, I am using the following configuration for adding the special tokens.
...ANSWER
Answered 2022-Jan-16 at 07:28For the important_tokens which contain several actual words (like frankie_and_bennys), you can replace underscore with the space and feed them normally, Or add them as a special token. I prefer the first option because this way you can use pre-trained embedding for their subtokens. For the ones which aren't actual words (like cb17dy), you must add them as special tokens.
QUESTION
Goal: Amend this Notebook to work with Albert and Distilbert models
Kernel: conda_pytorch_p36. I did Restart & Run All, and refreshed file view in working directory.
Error occurs in Section 1.2, only for these 2 new models.
For filenames etc., I've created a variable used everywhere:
...ANSWER
Answered 2022-Jan-13 at 14:10When instantiating AutoModel, you must specify a model_type parameter in ./MRPC/config.json file (downloaded during Notebook runtime).
List of model_types can be found here.
Code that appends model_type to config.json, in the same format:
QUESTION
I have this python file where I am trying to train a GPT2 model from scratch. For the same, I want to use gpu for faster acceleration and I am unable to do so. Help will be much appreciated
My python code is as follows.
PS : I am running this code on AWS Sagemaker, so I want to use their gpu acceleration.
I have used this for reference link
...ANSWER
Answered 2022-Jan-05 at 07:19You need to activate GPU runtime while hosting the notebook session in AWS SageMaker. The code will automatically take care of utilizing GPU resources.
Looking at the link which you shared - it doesn't have any custom configs to manually specify GPU resources.
If it's handled automatically by the framework which you're using to train the network, then in an active GPU session it will automatically allocate GPU resources while training.
QUESTION
I want to fine-tune the AutoModelWithLMHead model from this repository, which is a German GPT-2 model. I have followed the tutorials for pre-processing and fine-tuning. I have prepocessed a bunch of text passages for the fine-tuning, but when beginning training, I receive the following error:
...ANSWER
Answered 2022-Jan-04 at 14:08I didn't find the concrete answer to this question, but a workaround. For anyone looking for examples on how to fine-tune the GPT models from HuggingFace, you may have a look into this repo. They listed a couple of examples on how to fine-tune different Transformer models, complemented by documented code examples. I used the run_clm.py script and it achieved what I wanted.
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You can use gpt2 like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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