It would be helpful if you could help me add a GPA entity to Spacey Model for the named entity since there is no GPA entity in the resume dataset.
Assuming that you do have the resume dataset which is already annotated without GPA available with you.
You may annotate the entire train dataset for GPA and then train a new spacy model. Training a spacy model is straight forward please check https://spacy.io/usage/training#quickstart
If you do not have the resume dataset, you might want to create your own smaller resume dataset where you would only annotate GPA and train a new spacy model which only detects GPA. Then you can use your existing model to detect other entites and this model to detect GPAs. Note with this method you would parse your production data over 2 models.
To aid with your annotation process you can use any free annotation tools like Docanno(https://github.com/doccano/doccano) or Acharya(https://github.com/astutic/Acharya).
Related
I am trying to build a relation extraction model via spacy's prodigy tool.
NOTE: ner.manual, ner.correct, rel.manual are all recipes provided by prodigy.
(ner.manual, ner.correct) The first step involved annotating and training a NER model that can predict entities (this step is done and model is obtained)
The next step involved annotating the relations between the entities. Now, this step could be done int wo different method.
i. Label the entities and relations all from scratch
ii. Use the trained NER model to predict the entities in the UI tool and make corrections to it if needed (similar to ner.correct) and label the relations between the entities
The issue I am now facing is, whenever I use the trained model in the recipe's loop (rel.manual), there is no entities predicted.
Could someone help me with this??
PS: There is no trailing whitespaces issue, i cross-verified it
This question might be quite trivial and illogical, but I am a bit confused.
Does adding a article, which does not contain any entity tagged in it, into a NER training dataset add any value to model.
Does the model(specifically a spacy NER model) get some understanding of what not to tag as an entity out of this type of articles? Or is it not required and ignored while training?
I trained a custom SpaCy Named entity recognition model to detect biased words in job description. Now that I trained 8 variantions (using different base model, training model, and pipeline setting), I want to evaluate which model is performing best.
But.. I can't find any documentation on the validation of these models.
There are some numbers of recall, f1-score and precision on the meta.json file, in the output folder, but that is no sufficient.
Anyone knows how to validate or can link me to the correct documentation? The documentation seem nowhere to be found.
NOTE: Talking about SpaCy V3.x
During training you should provide "evaluation data" that can be used for validation. This will be evaluated periodically during training and appropriate scores will be printed.
Note that there's a lot of different terminology in use, but in spaCy there's "training data" that you actually train on and "evaluation data" which is not training and just used for scoring during the training process. To evaluate on held-out test data you can use the cli evaluate command.
Take a look at this fashion brands example project to see how "eval" data is configured and used.
How can I use the weights of a pre-trained network in my tensorflow project?
I know some theory information about this but no information about coding in tensorflow.
As been pointed out by #Matias Valdenegro in the comments, your first question does not make sense. For your second question however, there are multiple ways to do so. The term that you're searching for is Transfer Learning (TL). TL means transferring the "knowledge" (basically it's just the weights) from a pre-trained model into your model. Now there are several types of TL.
1) You transfer the entire weights from a pre-trained model into your model and use that as a starting point to train your network.
This is done in a situation where you now have extra data to train your model but you don't want to start over the training again. Therefore you just load the weights from your previous model and resume the training.
2) You transfer only some of the weights from a pre-trained model into your new model.
This is done in a situation where you have a model trained to classify between, say, 5 classes of objects. Now, you want to add/remove a class. You don't have to re-train the whole network from the start if the new class that you're adding has somewhat similar features with (an) existing class(es). Therefore, you build another model with the same exact architecture as your previous model except the fully-connected layers where now you have different output size. In this case, you'll want to load the weights of the convolutional layers from the previous model and freeze them while only re-train the fully-connected layers.
To perform these in Tensorflow,
1) The first type of TL can be performed by creating a model with the same exact architecture as the previous model and simply loading the model using tf.train.Saver().restore() module and continue the training.
2) The second type of TL can be performed by creating a model with the same exact architecture for the parts where you want to retain the weights and then specify the name of the weights in which you want to load from the previous pre-trained weights. You can use the parameter "trainable=False" to prevent Tensorflow from updating them.
I hope this helps.
for my current requirement, I'm having a dataset of 10k+ faces from 100 different people from which I have trained a model for recognizing the face(s). The model was trained by getting the 128 vectors from the facenet_keras.h5 model and feeding those vector value to the Dense layer for classifying the faces.
But the issue I'm facing currently is
if want to train one person face, I have to retrain the whole model once again.
How should I get on with this challenge? I have read about a concept called transfer learning but I have no clues about how to implement it. Please give your suggestion on this issue. What can be the possible solutions to it?
With transfer learning you would copy an existing pre-trained model and use it for a different, but similar, dataset from the original one. In your case this would be what you need to do if you want to train the model to recognize your specific 100 people.
If you already did this and you want to add another person to the database without having to retrain the complete model, then I would freeze all layers (set layer.trainable = False for all layers) except for the final fully-connected layer (or the final few layers). Then I would replace the last layer (which had 100 nodes) to a layer with 101 nodes. You could even copy the weights to the first 100 nodes and maybe freeze those too (I'm not sure if this is possible in Keras). In this case you would re-use all the trained convolutional layers etc. and teach the model to recognise this new face.
You can save your training results by saving your weights with:
model.save_weights('my_model_weights.h5')
And load them again later to resume your training after you added a new image to the dataset with:
model.load_weights('my_model_weights.h5')