when I try to run crfsharp, I get the following error at VS2012,
+err{"Could not find file 'C:\codeplex\POIParser\data\training\POIParser_corpus.train.tag'.":"C:\codeplex\POIParser\data\training\POIParser_corpus.train.tag"} System.Exception {System.IO.FileNotFoundException}
where can I find this file "POIParser_corpus.train.tag" ? I have downloaded both source code and main program of crfsharp and running it in VS2012.
Also I want to ask you can I use the CRFsharp to extract aspects by using training templates?
How do you run it ?
To train a CRF model, you need to prepare training corpus, template file at first and run CRFSharpConsole.exe with some parameters. CRFSharpConsole.exe will show usage, if you run it without any parameters.
Actually, I recommend you to download demo package from [DOWNLOADS] section in CRFSharp project web site(http://crfsharp.codeplex.com) at first, and then play with demo. In demo package, it will show you how to run CRFSharp in command line. For example, you can download Named entity recognized demo in English demo and run batch file to train a new model and test it.
For POIParser_corpus.train.tag you mentioned, it's the training corpus for Chinese POI inner-structure parser. You can also download it and run build_model.bat to train the model, and run test_model.bat to test it.
Related
I am trying to import models from hugging face and use them in Visual Studio Code.
I installed transformers, tensorflow, and torch.
I have tried looking at multiple tutorials online but have found nothing.
I am trying to run the following code:
from transformers import pipeline
classifier = pipeline('sentiment-analysis')
result = classifier("I hate it when I'm sitting under a tree and an apple hits my head.")
print(result)
However, I get the following error:
No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).
Using a pipeline without specifying a model name and revision in production is not recommended.
Traceback (most recent call last):
File "c:\Users\user\Desktop\Artificial Intelligence\transformers\Workshops\workshop_3.py", line 4, in <module>
classifier = pipeline('sentiment-analysis')
File "C:\Users\user\Desktop\Artificial Intelligence\transformers\src\transformers\pipelines\__init__.py", line 702, in pipeline
framework, model = infer_framework_load_model(
File "C:\Users\user\Desktop\Artificial Intelligence\transformers\src\transformers\pipelines\base.py", line 266, in infer_framework_load_model
raise ValueError(f"Could not load model {model} with any of the following classes: {class_tuple}.")
ValueError: Could not load model distilbert-base-uncased-finetuned-sst-2-english with any of the following classes: (<class 'transformers.models.auto.modeling_auto.AutoModelForSequenceClassification'>, <class 'transformers.models.auto.modeling_tf_auto.TFAutoModelForSequenceClassification'>, <class 'transformers.models.distilbert.modeling_distilbert.DistilBertForSequenceClassification'>, <class 'transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertForSequenceClassification'>).
I have already searched online for ways to set up transformers to use in Visual Studio Code but nothing is helping.
Do you know how to fix this error, or if someone knows how to successfully use models from Hugging Face into my code, it would be appreciated?
This question is a little less about Hugging Face itself and likely more about installation and the installation steps you took (and potentially your program's access to the cache file where the models are automatically downloaded to.).
From what I am seeing either:
1/ your program is unable to access the model
2/ your program is throwing specific value errors in a bit of an edge case
If 1/ Take a look here: [https://huggingface.co/docs/transformers/installation#cache-setup][1]
Notice that it the docs walks through where the pre-trained models are downloaded. Check that it was downloaded here: C:\Users\username\.cache\huggingface\hub (of course with your own username on your computer instead. Check in the cache location to make sure it was downloaded? (You can check in the cache locations mentioned.)
Second, if for some reason, there is an issue with downloading, you can try downloading manually and doing it via offline mode (this is more to get it up and running): https://huggingface.co/docs/transformers/installation#offline-mode
Third, if it is downloaded, do you have the right permissions to access the .cache? (Try running your program (if it is a program that you trust) on Windows Terminal as an administrator.). Various ways - find one that you're comfortable with, here are a couple hints from Stackoverflow/StackExchange: Opening up Windows Terminal with elevated privileges, from within Windows Terminal or this: https://superuser.com/questions/1560049/open-windows-terminal-as-admin-with-winr
If 2/ I have seen people bring up very specific issues on not finding specific values (not the same as yours but similar) and the issue was solved by installing PyTorch because some models only exist as PyTorch models. You can see the full response from #YokoHono here: Transformers model from Hugging-Face throws error that specific classes couldn t be loaded
I have used this project from Github: https://github.com/nicknochnack/TFODCourse
The project contains a model that can detect License Plate on a given Vehicle image. The Github repo also contains code for the conversion of model into Tensorflow Lite file.
I used that code to generate TFLite file.
And then, I followed this link: https://developers.google.com/codelabs/tflite-object-detection-android
Where I downloaded the sample Application of Object detection model and following the instructions, I copied my TFLite files into the Android Application.
Now, if I run the application and take a photo, it gives me this error,
/TaskJniUtils: Error getting native address of native library: task_vision_jni
java.lang.RuntimeException: Error occurred when initializing ObjectDetector: Input tensor has type kTfLiteFloat32: it requires specifying NormalizationOptions metadata to preprocess input images.
at org.tensorflow.lite.task.vision.detector.ObjectDetector
I understand that I have to add Metadata in my TFLite model. so, I searched about it and ended up on this link: https://www.tensorflow.org/lite/models/convert/metadata#model_with_metadata_format
But I didn't understand at all what exactly should I be doing. Can anyone please help me in pointing to the right direction that for my problem specifically, what exactly do I need to do?
I am using Google Colabs for my research in machine learning.
I do many variations on a network and run them saving the results.
I have a part of my notebook that used to be separate file (network.py) At the start of a training session I used to save this file in a directory that has the results and logs etc. Now that this part of the code is in the notebook it is easier to edit etc, BUT I do not have a file to copy to the output directory that describes the model. how to i take a section of a google colab notebook and save the raw code as a python file?
Things I have tried:
%%writefile "my_file.py" - is able to write the file however the classes are not available to the runtime.
I have been following github repository for "Tensorflow on Android".
I was able to build the code using bazel and then import the Android project to Android Studio, as mentioned here.
As you can see here, after building the APK, using Android Studio, the Model files/Graphs are included in the tensorflow/examples/android/assets
By default, tensorflow_inception_graph.pb and imagenet_comp_graph_label_strings.txt are included, from inception5 file which is downloaded while the APK is built.
What's the issue?
I have a retrained graph (InceptionV3 model, mentioned in tensorflow/examples/image_retraining/retrain.py), which I was able to place in the assets folder in android directory and generate a working APK.
Inference time while I was using the default graph or .pb file was ~500ms and with my retrained.pb or graph it is ~1400ms.(tested on OnePlus3T device)
Please help me understand
How to analyze the default tensorflow_inception_graph.pb on Tensorboard
Last May they have introduced a helper script called import_pb_to_tensorboard to do just that.
usage: import_pb_to_tensorboard.py [-h] [--model_dir MODEL_DIR]
[--log_dir LOG_DIR]
optional arguments:
-h, --help show this help message and exit
--model_dir MODEL_DIR
The location of the protobuf ('pb') model to
visualize.
--log_dir LOG_DIR The location for the Tensorboard log to begin
visualization from.
Note that currently, the version in master seems to have received more love than the one present in the latest 1.2.1 distribution of tensorflow, so I would suggest to use this one.
I am using Stanford Classifier for my project.
This project takes training data to tune the algorithm then test data to classify text inputs into categories.
So the format for test and training data is tab-delimited text which means predictor -TAB- input text
The software prints out the output to stdout (command line).
Is there anyway to output to a text file ?
I searched the javadoc of the project site, and I found
But I don't know how to use this property.
I tried -csvoutput=%1%n%c on command line
But it gives me java null pointer exception error when I try to run it.
If you want to save it to a file just add this to the end of your command:
> output_file.txt