simpletransformers | Language Modelling , Language Generation | Natural Language Processing library
kandi X-RAY | simpletransformers Summary
kandi X-RAY | simpletransformers Summary
Transformers for Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Perform prediction on the model
- Return the threshold if x is greater than the given threshold
- Calculate loss function
- Generate a dictionary of inputs
- Predict from to_predict
- Calculate the loss
- Convert tokens to word logits
- Get the current session object
- Predict outputs
- Generate the model
- Interactively interactively interactively
- Load a hdf5 dataset
- Convert model to onnx
- Interactively interactively
- Train the wandb run
- Encodes sentences
- Convert examples into Feature
- Convert the model to onnx
- Build a pandas DataFrame containing the hard coded values of the prediction
- Splits a document into titles and texts
- Train a new tokenizer
- Convert examples to features
- Train a model
- Runs prediction on the given dataset
- Basic transformer view
- Generates tokens from the model
- Builds a classification dataset
simpletransformers Key Features
simpletransformers Examples and Code Snippets
Lines of Code : 3
License : No License
Copypython train_tokenizer.py
model = BartModel(pretrained_model=None,args=model_args, model_config='config.json', vocab_file="./tokenize")
python train.py
"""
The script shows how to train Augmented SBERT (Domain-Transfer/Cross-Domain) strategy for STSb-QQP dataset.
For our example below we consider STSb (source) and QQP (target) datasets respectively.
Methodology:
Three steps are followed for AugSBER """
The script shows how to train Augmented SBERT (In-Domain) strategy for STSb dataset with BM25 sampling.
We utlise easy and practical elasticsearch (https://www.elastic.co/) for BM25 sampling.
Installations:
For this example, elasticsearch to be """
The script shows how to train Augmented SBERT (In-Domain) strategy for STSb dataset with Semantic Search Sampling.
Methodology:
Three steps are followed for AugSBERT data-augmentation strategy with Semantic Search -
1. Fine-tune cross-enco model = Seq2SeqModel(
encoder_decoder_type="marian",
encoder_decoder_name="Helsinki-NLP/opus-mt-en-mul",
args=args,
use_cuda=True,
)
# Input
to_predict = ["They went to the public swimming pool.", "model_args.use_multiprocessing = False
from multiprocessing import set_start_method
from multiprocessing import Process, Manager
try:
set_start_method('spawn')
except RuntimeError:
pass
@app.get("/article_classify")
def classification(text:str):
"""function to class# create X
X = df[]
# create y
y = df[]
# split to train and test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=123, stratify = y)
df = df1.append(df2)
Community Discussions
Trending Discussions on simpletransformers
QUESTION
I have a simple transformers script looking like this.
...ANSWER
Answered 2022-Feb-22 at 11:54Use this model instead.
QUESTION
When fine-tuning the sloBERTa Transformer model, based on CamemBERT, for a multiclass classification task with SimpleTransformers, I want to use the model argument "max_seq_length": 512, as previous work states that it gives better results than 128, but the inclusion of this argument triggers the error below. The error is the same in Kaggle and Google Colab environment, and terminating the execution and reruning it does not help. The error is triggered not matter how small the number of training epochs is, and the dataset contains only 600 instances (with text as strings, and labels as integers). I've tried lowering the max_seq_length to 509, 500 and 128, but the error persists.
The setup without this argument works normally and allows training with 90 epochs, so I otherwise have enough memory.
...ANSWER
Answered 2022-Jan-02 at 13:52This happened because max_seq_length defines the number of input neurons for the model thus increasing the number of trainable parameters which will require it to allocate more memory which might exceed your memory limits on those platforms.
Most of the time, max_seq_length is up the dataset, and sometimes adding too much could be wasteful in terms of training time and model size.
What you can do is to find the max number of words per sample in your training dataset and use that as your max_seq_length.
QUESTION
mt5 fine-tuning does not use gpu(volatile gpu utill 0%)
Hi, im trying to fine tuning for ko-en translation with mt5-base model. I think the Cuda setting was done correctly(cuda available is True) But during training, the training set doesn't use GPU except getting dataset first(very short time).
I want to use GPU resource efficiently and get advice about translation model fine-tuning here is my code and training env.
...ANSWER
Answered 2021-Nov-11 at 09:26it jus out of memory cases. The parameter and dataset weren't loaded on my gpu memory. so i changed my model mt5-base to mt5-small, delete save point, reduce dataset
QUESTION
I am creating a Question Answering model using simpletransformers. I would also like to use wandb to track model artifacts. As I understand from wandb docs, there is an integration touchpoint for simpletransformers but there is no mention of logging artifacts.
I would like to log artifacts generated at the train, validation, and test phase such as train.json, eval.json, test.json, output/nbest_predictions_test.json and best performing model.
...ANSWER
Answered 2021-Oct-20 at 11:26Currently simpleTransformers doesn't support logging artifacts within the training/testing scripts. But you can do it manually:
QUESTION
I am new to AI models and currently experimenting with the QandA model. Particularly I am interested in following 2 models.
1. from transformers import BertForQuestionAnswering
2. from simpletransformers.question_answering import QuestionAnsweringModel
Using option 1 BertForQuestionAnswering I am getting the desired results. However I can ask only one question at a time. Also I am not getting the probability of the answer.
below is the code for BertForQuestionAnswering from transformers.
...ANSWER
Answered 2021-Aug-28 at 13:27You can use the huggingface question answering pipeline to achieve that:
QUESTION
I'm currently working on a seminar paper on nlp, summarization of sourcecode function documentation. I've therefore created my own dataset with ca. 64000 samples (37453 is the size of the training dataset) and I want to fine tune the BART model. I use for this the package simpletransformers which is based on the huggingface package. My dataset is a pandas dataframe. An example of my dataset:
My code:
...ANSWER
Answered 2021-Jun-08 at 08:27While I do not know how to deal with this problem directly, I had a somewhat similar issue(and solved). The difference is:
- I use fairseq
- I can run my code on google colab with 1 GPU
- Got
RuntimeError: unable to mmap 280 bytes from file : Cannot allocate memory (12)immediately when I tried to run it on multiple GPUs.
From the other people's code, I found that he uses python -m torch.distributed.launch -- ... to run fairseq-train, and I added it to my bash script and the RuntimeError is gone and training is going.
So I guess if you can run with 21000 samples, you may use torch.distributed to make whole data into small batches and distribute them to several workers.
QUESTION
I'm trying to use SimpleTransformers default setup to do multi-task learning.
I am using the example from their website here
The code looks like below:
...ANSWER
Answered 2021-May-30 at 17:54In the example code if you change
QUESTION
I am trying to run a service that uses simple transformers Roberta model to do classification. the inferencing script/function itself is working as expected when tested. when i include that with fast api its shutting down the server.
...ANSWER
Answered 2021-Jan-10 at 11:49put the entire function under a try-except block and show the output so we can investigate the real issue.
QUESTION
I'm trying to execute the simpletransformers example from their site on google colab.
Example:
...ANSWER
Answered 2021-Jan-29 at 14:27I am putting this here incase someone faces the same problem. I was helped by the creator himself.
QUESTION
I am fine-tuning BERT on a financial news dataset. Unfortunately BERT seems to be trapped in a local minimum. It is content with learning to always predict the same class.
- balancing the dataset didnt work
- tuning parameters didnt work as well
I am honestly not sure what is causing this problem. With the simpletransformers library I am getting very good results. I would really appreciate if somebody could help me. thanks a lot!
Full code on github: https://github.com/Bene939/BERT_News_Sentiment_Classifier
Code:
...ANSWER
Answered 2020-Nov-07 at 13:37For multi-class classification/sentiment analysis using BERT the 'neutral' class HAS TO BE 2!! It CANNOT be between 'negative' = 0 and 'positive' = 2
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install simpletransformers
You can use simpletransformers 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.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
