I have trained a text classification model using blazingText on AWS sagemaker, I can load the trained model and deploy an inference endpoint
model = bt_model.deploy(initial_instance_count=1, endpoint_name=endpoint_name, instance_type='ml.m5.xlarge', serializer=JSONSerializer())
payload = {"instances": terms}
response = model.predict(payload)
predictions = json.loads(response)
and it's working fine, now I need to load the model's bin file using an entry_point in order to do some logic before and after predictions in the input_fn and output_fn.
I extracted the bin file from the model.tar.gz and I can load it, but I get Segmentation Fault when I try to run a prediction
from gensim.models import FastText
from gensim.models.fasttext import load_facebook_model, load_facebook_vectors
model=FastText.load('model.bin')
model.predict('hello world')
As per blazingText documentations
For both supervised (text classification) and unsupervised (Word2Vec)
modes, the binaries (*.bin) produced by BlazingText can be
cross-consumed by fastText and vice versa. You can use binaries
produced by BlazingText by fastText. Likewise, you can host the model
binaries created with fastText using BlazingText.
Here is an example of how to use a model generated with BlazingText
with fastText:
#Download the model artifact from S3 aws s3 cp s3://<YOUR_S3_BUCKET>//model.tar.gz model.tar.gz
#Unzip the model archive tar -xzf model.tar.gz
#Use the model archive with fastText fasttext predict ./model.bin test.txt
but for some reason it's not working as expected
Related
I'd like to retrain a YOLOv7-tiny Object Detector on a custom dataset with 4 classes.
According to the README of the official git repo in order to transfer learn one should pass the pre-trained weights as argument:
python train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights 'yolov7_training.pt' --name yolov7-custom --hyp data/hyp.scratch.custom.yaml
As for the normal YOLOv7 model, there are the weight files yolov7.pt and yolov7_training.pt present in the repo (see here). What´s the difference between these two and which should be used in which occasion?
Also there's a yolov7-tiny.pt weight file, but no respective yolov7-tiny_training.pt file. Is there a particular reason for that?
Also I learned that for Transfer Learning it's helpful to "freeze" the base models weights (make them untrainable) first, then train the new model on the new dataset, so only the new weights get adjusted. After that you can "unthaw" the frozen weights to fine-tune the entire model. The train.py script has a --freeze argument to freeze backbone layers.
Is this approach recommended for retraining YOLOv7? If so, should you freeze all 50 backbone layers of YOLOv7 (and would that command be --freeze 50 or smth. different)? And how would you then unthaw these layers and resume the training of said model?
I want to download a pretrained a model and fine tune the model with my own data. I have downloaded a bert-large-NER model artifacts from hugging face,I have listed the contents below . being new to this, I want to know what files or artifacts do i need and from the looks of it the pytorch_model.bin is the trained model, but what are these others file and their purpose like tokenizer files and vocab.txt ....
config.json
pytorch_model.bin
special_tokens_map.json
tokenizer_config.json
vocab.txt
These different files are the metadata of your model and the tokenizer that you are using (when you serialize your model this is the output). To fine tune a pre-trained model from the HF Hub you can either use PyTorch or TF or also the Trainer class where you don't have to write your own custom training code. Ex:
trainer = Trainer(
model=model,
args=training_args,
train_dataset=small_train_dataset,
eval_dataset=small_eval_dataset,
compute_metrics=compute_metrics,
)
Reference the official docs here as well for understanding how to tune a pre-trained model end to end: https://huggingface.co/docs/transformers/training.
I fine-tuned a pretrained BERT model in Pytorch using huggingface transformer. All the training/validation is done on a GPU in cloud.
At the end of the training, I save the model and tokenizer like below:
best_model.save_pretrained('./saved_model/')
tokenizer.save_pretrained('./saved_model/')
This creates below files in the saved_model directory:
config.json
added_token.json
special_tokens_map.json
tokenizer_config.json
vocab.txt
pytorch_model.bin
Now, I download the saved_model directory in my computer and want to load the model and tokenizer. I can load the model like below
model = torch.load('./saved_model/pytorch_model.bin',map_location=torch.device('cpu'))
But how do I load the tokenizer? I am new to pytorch and not sure because there are multiple files. Probably I am not saving the model in the right way?
If you look at the syntax, it is the directory of the pre-trained model that you are supposed to pass. Hence, the correct way to load tokenizer must be:
tokenizer = BertTokenizer.from_pretrained(<Path to the directory containing pretrained model/tokenizer>)
In your case:
tokenizer = BertTokenizer.from_pretrained('./saved_model/')
./saved_model here is the directory where you'll be saving your pretrained model and tokenizer.
everyone, I am new to caffe. Currently, I try to use the trained GoogleNet which was downloaded from model zoo to classify some images. However, the network's output seem to be a vector rather than real label(like dog, cat).
Where can I find the label-map between trained model like googleNet's output to their real class label?
Thanks.
If you got caffe from git you should find in data/ilsvrc12 folder a shell script get_ilsvrc_aux.sh.
This script should download several files used for ilsvrc (sub set of imagenet used for the large scale image recognition challenge) training.
The most interesting file (for you) that will be downloaded is synset_words.txt, this file has 1000 lines, one line per class identified by the net.
The format of the line is
nXXXXXXXX description of class
I am new to Weka. I am trying to classify text documents after OCR process. The training corpus contains 286 mortgage documents and 57 note documents. The test dataset contains 1-100 text pages. So each line of the training and test dataset contains few paragraphs of text data. After classification text documents should be classified into mortgage or note properly.
I am doing a StringToWordVector operation combining both Training and Test dataset with missing values from Test dataset i.e. "?".
Steps are as follows:
Create training Arff file using following command line:
java -cp weka.jar weka.core.converters.TextDirectoryLoader -dir <text directory>
This creates a training dataset with known classes i.e. mortgage, note
Create test Arff file with missing classes i.e "?"
Combine both training and test dataset
Run the classifier with following command line:
java -cp weka.jar weka.classifiers.meta.FilteredClassifier -t train.arff -test.arff -F "weka.filters.MultiFilter -F weka.filters.unsupervised.attribute.StringToWordVector -F weka.filters.unsupervised.attribute.Standardize" -d trained.model -p 0
I am running the above example from both Weka GUI and from command line as well. Everything works fine as far as commands are concerned. The results are abnormal. Not at all correct.
I have also tried to run StringToWordVector operation separately and tested through NaiveBayes, NaiveBayesMultiNomial, J48 and other multiclass classifiers on the dataset but classification prediction is not correct. Always giving abnormal results.
Please help me to get the proper prediction result. Let me know if the above steps are correct and if I am doing anything wrong.