IntentClassification

Project for NLP course 2022-2023 AIDL. Using a custom dataset, classify Questions as samples into some categories Task : Multiclass

This Notebook includes an implementation with a custom LSTM and pretrained embeddings. The basic tools for ML where Tensorflow + Tensorflow hub

This Notebook includes an implementation with huggingface pretrained transformers. The basic tools for ML where Pytorch + HuggingFace

Here you can find the presentation for this project

You can open one of them and do the trainings. You can also find results in my gdrive:

Output Files :

Here is the lstm and the PretrainedEmb model in tensorflow:

Here is huggingface transformers models:

Here are the results metrics plots for transformers and all the metrics in a csv:

.mar

Torch Serve

Create a conda environment with python 3.10 (it may also work in other python versions but I have not tested in other versions)

1. Install all the requirements using this command :

pip install -r requirements.txt

Run this in your current conda env: conda install -c pytorch torchserve torch-model-archiver torch-workflow-archiver

2. Run this on terminal if you have your model (Plz update with your model path after model/ , the example is with roberta)

torch-model-archiver --model-name "roberta_intent" \
                     --version 1.0 --serialized-file ./model/roberta-base-20230208T123209Z-001/roberta-base/pytorch_model.bin \
                     --extra-files "./model/roberta-base-20230208T123209Z-001/roberta-base/config.json, ./model/index_to_name.json, ./src/preprocessing_tools.py" \
                     --handler "./app/handler.py"

The previous command will produce a file named eirini_roberta.mar that can be understood by TorchServe. If you want to change the name of the model change eirini_roberta to what you like most. 3. Then move this mar file into deployment/model-store folder (make sure there is model-store folder)

mv roberta_intent.mar ./deployment/model-store 

4. Now it is time for torch serve

torchserve  --start \
            --model-store  ./deployment/model-store \
            --ts-config ./deployment/config.properties \
            --models roberta_intent=roberta_intent.mar

5. Finally ping in port and the status should be healthy

curl http://localhost:8080/ping

Streamlit

You can use the served model by running

streamlit run app/main.py

Finally you can

torchserve --stop