*** I used a pretrained BertModel to fine-tune model on squadv1 then fine-tune result model to final model.
This how I can load my trined model on squadv1
output_dir="mybert_squadv1/checkpoint-60000/"
model = AutoModelForQuestionAnswering.from_pretrained(output_dir)
tokenizer = AutoTokenizer.from_pretrained(output_dir, do_lower_case=do_lower_case)
after I loaded my model on a dataset, I want to fine-tune this model on another dataset. Let say squadv3
Can I do that or not ? if so, how i can do it?
you just get the tokenizer.
After that,you should add your own Neural Networks after that and loss function.
you can see how to train a normal network.
Related
I'm trying to find information on how to train a BERT model, possibly from the Huggingface Transformers library, so that the embedding it outputs are more closely related to the context o the text I'm using.
However, all the examples that I'm able to find, are about fine-tuning the model for another task, such as classification.
Would anyone happen to have an example of a BERT fine-tuning model for masked tokens or next sentence prediction, that outputs another raw BERT model that is fine-tuned to the context?
Thanks!
Here is an example from the Transformers library on Fine tuning a language model for masked token prediction.
The model that is used is one of the BERTForLM familly. The idea is to create a dataset using the TextDataset that tokenizes and breaks the text into chunks. Then use a DataCollatorForLanguageModeling to randomly mask tokens in the chunks when traing, and pass the model, the data and the collator to the Trainer to train and evaluate the results.
I am working on a project involving neural machine translation (translating English to French).
I have worked through some examples online, and have now finished the model. Once a model is trained using Keras, how do I then get a prediction of a translation without training the entire model again, because with the large dataset I am using, each epoch takes some time and of course, I can't train the model every time I want a translation.
So what is the correct way of then generating predictions on new inputs without training the whole model again?
Thanks
You need to save your model the model and its weights when the fit ends using :
keras.model.save(model_name)
At any time, you can load your trained model using
model = keras.load(model_name)
then perform predictions as
y_pred = model.predict(x_test)
Hope this will be helpful
You can use the .predict() function which you can pass new inputs into it and it give you a prediction. The docs for this function are here: keras
I have already trained a deep learning model with some data and it performs well with the test data. Now, how do I retrain this model when I get new data?
You can Save your model using
keras.model.save(yourModel, 'fileName.hdf5')
After you got the data you can load your saved model
model = keras.model.load_model('fileName.hdf5')
model.fit()
The training will continue from last saved weights, optimizer and loss.
I am building a CNN model with Keras and tensorflow backend. I've trained the model for 6 hours. Now, I want to predict my custom external image using my model. How to do that in Keras? Thank you
To simplify, you could say there is usually 4 main steps when working with models (not just in Keras) :
building the model and compiling it : model.compile()
training the model with your training data : model.fit()
evaluating the model with your test data : model.evaluate()
making predictions with your target data : model.predict()
Depending on which output you need, you will have to use model.predict_proba() or model.predict_classes().
See https://keras.io/models/sequential/#sequential-model-methods for complete reference and arguments.
Keras repository has also plenty of examples : https://github.com/fchollet/keras/tree/master/examples
I am using Gensim Library in python for using and training word2vector model. Recently, I was looking at initializing my model weights with some pre-trained word2vec model such as (GoogleNewDataset pretrained model). I have been struggling with it couple of weeks. Now, I just searched out that in gesim there is a function that can help me to initialize the weights of my model with pre-trained model weights. That is mentioned below:
reset_from(other_model)
Borrow shareable pre-built structures (like vocab) from the other_model. Useful if testing multiple models in parallel on the same corpus.
I don't know this function can do the same thing or not. Please help!!!
You can now do incremental training with gensim. I would recommend loading the pretrained model and then doing an update.
from gensim.models import Word2Vec
model = Word2Vec.load('pretrained_model.emb')
model.build_vocab(new_sentences, update=True)
model.train(new_sentences)