I need to get confidence about each extracted entity (not to print it but to get it), however, I can't find a method that returns confidences.
Firstly, I have tried using Stanford Named Entity Recognizer library on Java and this solution:
Display Stanford NER confidence score
but it doesn't work (I guess getCliqueTree method is not available). I also have tried using NLTK in Python and Stanford NER model to extract entities, but again couldn't find a way to get confidences.
I know how to do it on Spacy:
https://github.com/explosion/spaCy/issues/831
but as the author says it's inefficient.
So, can you please advise me, how to get the probabilities of each extracted entity?
Usually NER is a token level classification task.
Confidences are usually derived from each prediction, which is commonly the output of some type of softmax.
The issue then become, how can I get a confidence for a sequence of confidences?
There are multiple ways:
Entropy [Confidence is amount of information]
Average (Mean) [Confidence is the average]
Min/Max of confidences [Confidence is the min/max]
All of these give different answers, none are "better" and it really depends on your use case.
If you would like to order possible entity types, you can start with the following:
Get confidences assuming same label for each token
Get entropy for confidence (probability) sequence
Sort by entropy
Related
I am conducting the ordinal regression model using the "clmm" function of the package "ordinal". As the tutorial said, we should use the "nominal_test" and "scale_test" in the "clm" function to check the assumption.
Like the result of the example in this tutorial, unfortunately, the p-values of some predictors are significant in "nominal_test" and "scale_test" in my case. According to Fabian Bross (2019, www.fabianbross.de/mixedmodels.pdf.), "if the proportional odds assumption fails, the results of the model will not be reliable."
Then I also tried to check the assumption by the "brant" function in the package "brant." Again, some predictors are significant, which means my model is not reliable.
As I have to fit the ordinal regression model with random factors, it is difficult for me to give the "clmm" model up. I am unsure about the importance of checking assumptions and what I should do next, as the model failed to check the assumption.
I have been looking for certain features in the HuggingFace transformer Trainer object (in particular Seq2SeqTrainer) and would like to know whether they exist and if so, how to implement them, or whether I would have to write my own training loop to enable them.
I am looking to apply Curriculum Learning to my training strategy, as well as evaluating the model at regular intervals, and therefore would like to enable the following
choose in which order the model sees training samples at each epoch (it seems that the data passed onto the train_dataset argument are automatically shuffled by some internal code, and even if I managed to stop that, I would still need to pass differently ordered data at different epochs, as I may want to start training the model from easy samples for a few epochs, and then pass a random shuffle of all data for later epochs)
run custom evaluation at integer multiples of a fix number of steps. The standard compute_metrics argument of the Trainer takes a function to which the predictions and labels are passed* and the user can decide how to generate the metrics given these. However I'd like a finer level of control, for example changing the maximum sequence length for the tokenizer when doing the evaluation, as opposed to when doing training, which would require me including some explicit evaluation code inside compute_metrics which needs to access the trained model and the data from disk.
Can these two points be achieved by using the Trainer on a multi-GPU machine, or would I have to write my own training loop?
*The function often looks something like this and I'm not sure it would work with the Trainer if it doesn't have this configuration
def compute_metrics(eval_pred):
predictions, labels = eval_pred
...
You can pass custom functions to compute metrics in the training arguments
I have dataset (sequence to sequence), each sample input is seq of charterers (combination from from 20 characters and max length 2166) and out is list of charterers (combination of three characters G,H,B). for example OIREDSSSRTTT ----> GGGHHHHBHBBB
I would like to do simple pytorch model that work in that type of dataset. Model that can predict sequence of classes. I would appreciate any suggestions or links for simple mode that do the same?
Thanks
If the output sequence always has the same length as the input sequence, you might want to use transformer encoder, because it basically transforms the inputs with attention to the context. Also you can try to use anything that is used to tagging: BiLSTM, BiGRU, etc.
If you want your model to be able to predict sequences of different length (not necessary the same as input length), look at some encoder-decoder models, such as vanilla transformer.
You can start with the sequence tagging model from PyTorch tutorial https://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html .
As #Ilya Fedorov said, you can move to transformer models for potentially better performance.
Is deep learning model supports multi-label classification problem or any other algorithms in H2O?
Orginal Response Variable -Tags:
apps, email, mail
finance,freelancers,contractors,zen99
genomes
gogovan
brazil,china,cloudflare
hauling,service,moving
ferguson,crowdfunding,beacon
cms,naytev
y,combinator
in,store,
conversion,logic,ad,attribution
After mapping them on the keys of the dictionary:
Then
Response variable look like this:
[74]
[156, 89]
[153, 13, 133, 40]
[150]
[474, 277, 113]
[181, 117]
[15, 87, 8, 11]
Thanks
No, H2O only contains algorithms that learn to predict a single response variable at a time. You could turn each unique combination into a single class and train a multi-class model that way, or predict each class with a separate model.
Any algorithm that creates a model that gives you "finance,freelancers,contractors,zen99" for one set of inputs, and "cms,naytev" for another set of inputs is horribly over-fitted. You need to take a step back and think about what your actual question is.
But in lieu of that, here is one idea: train some word embeddings (or use some pre-trained ones) on your answer words. You could then average the vectors for each set of values, and hope this gives you a good numeric representation of the "topic". You then need to turn your, say, 100 dimensional averaged word vector into a single number (PCA comes to mind). And now you have a single number that you can give to a machine learning algorithm, and that it can predict.
You still have a problem: having predicted a number, how do you turn that number into a 100-dim vector, and from there in to a topic, and from there into topic words? Tricky, but maybe not impossible.
(As an aside, if you turn the above "single number" into a factor, and have the machine learning model do a categorization, to predicting the most similar topic to those it has seen before... you've basically gone full circle and will get a model identical to the one you started with that has too many classes.)
I'm implementing LDA using Java. I know how the algorithm works. In the end of the training (the given iterations) I will get 2 matrices (topic-word and document-topic) that represent the set of the input documents.
My problem is that when I input a new document (query) I want to use these matrices (or any other way) to get the document-topic vector of that query. How would I do that?
Are you using Variational Inference or Gibbs Sampling?
For Gibbs Sampling a typical approach is adding the new document/s to the inference, and only updating its own counters, keeping constant the counters for the documents you used to learn the model.
This is specified in equations 84 and 85 in Parameter Estimation for Text Analysis
I guess there has to be a similar approach in VI LDA.