I'm working on a custom NER model with spacy-transformers and roBERTa. I'm really only using the CLI for this and am trying to alter my Spacy config.cfg file to account for custom entity labels in the pipeline.
I'm new to Spacy, but I've gathered that people usually use ner.add_label to accomplish this. I wonder if I might be able to change something in [initialize.components.ner.labels] of the config, but haven't come across a good way to do that.
I can't seem to find any options to alter the config file in a similar fashion - does anyone know if this is possible, or what might be the most succinct way to achieve those custom labels?
Edited for clarity: My issue could be different than my config theory. Right now I am getting an output, but instead of text labels they are numeric labels, such as:
('Oct',383) ('2019',383) ('February',383)
Thank you in advance for your help!
If you are working with the config-based training, generally you should not have to specify the labels anywhere - spaCy will look at the training data and get the list of labels from there.
There are a few cases where this won't work.
You have labels that aren't in your training data. These can't be learned so I would just consider this an error, but sometimes you have to work with the data you've been given.
You training data is very large. In this case reading over all the training data to get a complete list of labels can be an issue. You can use the init labels command to generate data so that the input data doesn't have to be scanned every time you start training.
Related
I'm training a neural network using keras but I'm not sure how to feed the training data into the model in the way that I want.
My training data set is effectively infinite, I have some code to generate training examples as needed, so I just want to pipe a continuous stream of novel data into the network. keras seems to want me to specify my entire dataset in advance by creating a numpy array with everything in it, but this obviously wont work with my approach.
I've experimented with creating a generator class based on keras.utils.Sequence which seems like a better fit, but it still requires me to specify a length via the __len__ method which makes me think it will only create that many examples before recycling them. Can someone suggest a better approach?
If I save a xgboost model in mlflow with mlflow.xgboost.log_model(model, "model") and load it with model = mlflow.xgboost.load_model("models:/model_uri") and want to plot the feature importance with xgboost.plot_importance(model) the problem is that the features are not shown with names (see plot). If I plot the feature without saving in mlflow the origin feature names are shown. Do I have to store the model in another way?
Usually, if you use a pipeline It can happen for example on FeatureUnion from sklearn.
You can try to get the feature index from the model or the last step of the pipeline and use it to retrieve the feature names from the dataset.
If you are using a pipeline you can try to get the feature the step before this problem appears or edit the step, also be aware if you are using feature selection different situations can happen.
You can use autologging to autosave the plot, but the same problem happen if it is pipeline.
You could save the model like an artifact, if you think to do it, my suggestion is to use the Dill package.
In https://www.tensorflow.org/js/guide/save_load, it describes the format for saving model files as one that uses model.json and the corresponding model.weights.bin. I'm not sure if there's a name for talking about this format (I think it's the same as https://js.tensorflow.org/api/latest/#class:LayersModel but not entirely certain), and I'm wondering if there's a way to visualize them as a graph.
I was expecting to be able to load and view them in TensorBoard but don't see any way to do this with its "Graphs" tool, so perhaps no one has made anything like this yet.
One minimal way to do this is with the summary method on a loaded model, which will log to the console:
In the example I tried, this output doesn't match what I would expect from looking at the model.json though. It looks like this might only look at the outermost Sequential layer but I didn't look more closely.
You can examine models, layers and tensors and etc via Visor API https://js.tensorflow.org/api_vis/latest/. You just need to install it npm i #tensorflow/tfjs-vis in order to use.
I am working on a project that aims to detect objects in certain difficult circumstances. I ran a test with Mask_RCNN on a dataset that contains that specific type of difficult examples and it did a pretty good job in some of them.
But some other examples didn't get detected surprisingly, when there is no obvious reason. To understand the reason behind this performance difference, I've been adviced to use Tensorboard. But then I realized that its mostly used for training phase, as I understood from this video.
At the end of the video, however, they mention about an integration project of Tensorboard, namely the Tensorflow Debugger Integration. But unfortunately I could not find further information regarding the continuation about that feature.
Is there any way to visualize weights and activation maps inside a CNN during inference/evaluation phase?
The main difference between training and inference time for tensorboard will be the global_step value. Most graphs display global step as the x-axis. You can supply your own global step counter if you like, but you'll have to decide what the x-axis should represent to you in this case since "time" isn't really a logical construct during inference. Other tabs such as the images tab don't have a time component, so using them should be the same as during training.
The tensorflow debugger is a nice terminal debugger, but wouldn't really be related to what you're trying to do here. It's certainly not a visualization tool.
Another approach might be to simply generate your own plots and output a set of PDFs with the various visualizations you need using standard tools like matplotlib for each test image. I've found tools like XnView make it really easy to look through a lot of PDF visualizations to understand what's going on. I've used this approach quite effectively. If you want to view many hundreds or thousands of results quickly you might have an easier time if all the visuals are just dumped out to a directory.
I want to use the textsum model for tagging named entities. Hence the target size vocab is very small. While training there doesn't seem to be an option to provide different vocabs on the encoder and on the decoder side-or is there?
See code lines on Github
if hps.mode == 'train':
model = seq2seq_attention_model.Seq2SeqAttentionModel(hps, vocab, num_gpus=FLAGS.num_gpus)
Hrishikesh I don't believe there is a way to provide separate vocab files, but not fully understanding why you need it. The vocab simply provides a numerical way of representing a word. Therefore when the model is working with these words, it uses the numerical representations of them. Once the hypothesis is complete and the statistical choices for words have been chosen, it then simply uses the vocab file to convert back the vocab index to it's associated word. I hope this helps answer your question and solidify why you shouldn't need to have separate vocab files. That said, I may jsut be misunderstanding your need for it and I apologize if that is the case.
No there is no out-of-the-box option to use the textsum in this way. I don't see any reason why it shouldn't be possible to modify the architecture to achieve this, though. Would be interested if you pointed towards some literature on using seq2seq w/attention models for NER