I have a use case that to use autoML tables to train the model for multi-label classification. I don't see this option when creating the dataset. Is it supported?
Related
I'm currently training two models (BERT & MPNet) for a semantic textual similarity (STS) task using the SentenceTransformers library.
Now I want to check, if the base models and/or the trained models understand specific words/names which occur within the training dataset. I tried masking or calculating the similarity to specific categories related to the words/names but the results were hardly distinguishable.
Is there any way to check or even prove if a model understands a specific word or sequence?
Current BERT base uncased clinical NER predict clinical entities( Problem, Test, Treatment)
I want to train on different clinical dataset to get entity like ( Disease, Medicine, Problem)
How to achieve that??
Model
There are several models in Huggingface which are trained on medical specific articles, those will definitely perform better than normal bert-base-uncased. BioELECTRA is one of them and it managed to outperform existing biomedical NLP models in several benchmark tests.
There are 3 different versions of those models depending on their pretraining dataset. But I think these 2 will be the best to start with.
Bioelectra-base-discriminator-pubmed: Pretrained on pubmed
Bioelectra-base-discriminator-pubmed-pmc: Pretrained on pubmed and pmc
NER Datasets:
Now coming to NER dataset there are several dataset you might like or you might want to create a composite dataset. Some of these are -
BC5-disease, NCBI-disease, BC5CDR-disease from BLUE benchmark
[Let me know if you need any help with model creation or setting up the finetuning setup. Also please use proper metrics to evaluate them and do share the metrics dashboard after it gets finished.]
I'm currently comparing various pre-trained NMT models and can't help but wonder what the difference between MarianMT and OpusMT is. According to OpusMT's Github it is based on MarianMT. However in the Huggingface transformers implementation all pretrained MarianMT models start with "Helsinki-NLP/opus-mt". So I thought it was the same, but even though they're roughly the same size, they yield different translation results.
If someone could please shed some light on what the differences are I would be very thankful.
Marian is an open-source tool for training and serving neural machine translation, mostly developed at the University of Edinburgh, Adam Mickiewicz University in Poznań and at Microsoft. It is implemented in C++ and is heavily optimized for MT, unlike PyTorch-based Huggingface Transformers that aim for generality rather than efficiency in a specific use case.
The NLP group at the University of Helsinki trained many translation models using Marian on parallel data collected at Opus, and open-sourced those models. Later, they also did a conversion of the trained model into Huggingface Transformers and made them available via the Huggingface Hub.
MarianMT is a class in Huggingface Transformers for imported Marian models. You can train a model in Marian and convert it yourself. OpusMT models are Marian models trained on the Opus data in Helsinki converted to the PyTorch models. If you search the Huggingface Hub for Marian, you will find other MarianMT models than those from Helsinki.
I understand that Stanford NER only supports training through a file... is there a way to add more training data at a later stage to update the NER model once it is already trained?
I understand that I can keep all the training datasets from the past and re-train the model, but, I am wondering if there is a way to update the NER model rather than retrain it from scratch.
For the larger audience: StanfordNER does not support Online Training. Marking this question as closed.
I am planning to use Google Prediction API for Sentiment Analysis. How can I generate the Traning model for this? Or where can I have any standard training model available for commercial use? I have already tried with the Sentiment Predictor provided in Prediction Gallery of Google Prediction API, but does not seem to work properly.
From my understanding, the "model" for the Google Prediction API is actually not a model, but a suite of models for regression as well as classification. That being said, it's not clear how the Prediction API decides what kind of regression or classification model is used when you present it with training data. You may want to look at how to train a model on the Google Prediction API if you haven't already done so.
If you're not happy with the results of the Prediction API, it might be an issue with your training data. You may want to think about adding more examples to the training file to see if the model comes up with better results. I don't know how many examples you used, but generally, the more you can add, the better.
However, if you want to look at creating one yourself, NLTK is a Python library that you can use to train your own model. Another Python library you can use is scikit-learn.
Hope this helps.
google prediction API is great BUT to train a model you will need...LOT OF DATA.
you can use the sentiment model that is alrady trained..