diff --git a/.github/workflows/test_package.yaml b/.github/workflows/test_package.yaml
index e7de7cdf..9afee0c8 100644
--- a/.github/workflows/test_package.yaml
+++ b/.github/workflows/test_package.yaml
@@ -15,7 +15,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v3
diff --git a/Dockerfile b/Dockerfile
index 13b549d9..d19f577b 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -13,6 +13,6 @@ COPY current_best_model/final_svc/final_svc.sav /final_svc.sav
 COPY current_best_model/final_xgb/final_xgb.sav /final_xgb.sav
 COPY --chmod=755 docker_run.py docker_run.py
 
-LABEL org.opencontainers.image.source=https://github.com/cdu-data-science-team/pxtextmining
+LABEL org.opencontainers.image.source=https://github.com/the-strategy-unit/pxtextmining
 
 ENTRYPOINT ["python3", "docker_run.py"]
diff --git a/api/api.py b/api/api.py
index 730d3962..47fea68f 100644
--- a/api/api.py
+++ b/api/api.py
@@ -3,9 +3,9 @@
 from typing import List
 
 import pandas as pd
+import schemas
 from fastapi import FastAPI
 
-from api import schemas
 from pxtextmining.factories.factory_predict_unlabelled_text import (
     predict_multilabel_sklearn,
 )
@@ -31,12 +31,12 @@
     version="1.0.0",
     contact={
         "name": "Patient Experience Qualitative Data Categorisation",
-        "url": "https://cdu-data-science-team.github.io/PatientExperience-QDC/",
+        "url": "https://the-strategy-unit.github.io/PatientExperience-QDC/",
         "email": "chris.beeley1@nhs.net",
     },
     license_info={
         "name": "MIT License",
-        "url": "https://github.com/CDU-data-science-team/pxtextmining/blob/main/LICENSE",
+        "url": "https://github.com/the-strategy-unit/pxtextmining/blob/main/LICENSE",
     },
     openapi_tags=tags_metadata,
 )
diff --git a/api/requirements.txt b/api/requirements.txt
index 1ee2e4d6..349ee3b3 100644
--- a/api/requirements.txt
+++ b/api/requirements.txt
@@ -1,100 +1,98 @@
-absl-py==2.0.0 ; python_version >= "3.8" and python_version < "3.11"
-anyio==4.0.0 ; python_version >= "3.8" and python_version < "3.11"
+absl-py==2.1.0 ; python_version >= "3.8" and python_version < "3.11"
+anyio==4.2.0 ; python_version >= "3.8" and python_version < "3.11"
 astunparse==1.6.3 ; python_version >= "3.8" and python_version < "3.11"
-cachetools==5.3.1 ; python_version >= "3.8" and python_version < "3.11"
-certifi==2023.7.22 ; python_version >= "3.8" and python_version < "3.11"
+cachetools==5.3.2 ; python_version >= "3.8" and python_version < "3.11"
+certifi==2024.2.2 ; python_version >= "3.8" and python_version < "3.11"
 cfgv==3.4.0 ; python_version >= "3.8" and python_version < "3.11"
-charset-normalizer==3.3.0 ; python_version >= "3.8" and python_version < "3.11"
+charset-normalizer==3.3.2 ; python_version >= "3.8" and python_version < "3.11"
 click==8.1.7 ; python_version >= "3.8" and python_version < "3.11"
-colorama==0.4.6 ; python_version >= "3.8" and python_version < "3.11" and sys_platform == "win32" or python_version >= "3.8" and python_version < "3.11" and platform_system == "Windows"
+colorama==0.4.6 ; python_version >= "3.8" and python_version < "3.11" and (sys_platform == "win32" or platform_system == "Windows")
 contourpy==1.1.1 ; python_version >= "3.8" and python_version < "3.11"
-coverage[toml]==7.3.2 ; python_version >= "3.8" and python_version < "3.11"
-cycler==0.12.0 ; python_version >= "3.8" and python_version < "3.11"
-distlib==0.3.7 ; python_version >= "3.8" and python_version < "3.11"
-exceptiongroup==1.1.3 ; python_version >= "3.8" and python_version < "3.11"
+coverage[toml]==7.4.1 ; python_version >= "3.8" and python_version < "3.11"
+cycler==0.12.1 ; python_version >= "3.8" and python_version < "3.11"
+distlib==0.3.8 ; python_version >= "3.8" and python_version < "3.11"
+exceptiongroup==1.2.0 ; python_version >= "3.8" and python_version < "3.11"
 fastapi==0.101.1 ; python_version >= "3.8" and python_version < "3.11"
-filelock==3.12.4 ; python_version >= "3.8" and python_version < "3.11"
+filelock==3.13.1 ; python_version >= "3.8" and python_version < "3.11"
 flatbuffers==23.5.26 ; python_version >= "3.8" and python_version < "3.11"
-fonttools==4.43.0 ; python_version >= "3.8" and python_version < "3.11"
-fsspec==2023.9.2 ; python_version >= "3.8" and python_version < "3.11"
+fonttools==4.48.1 ; python_version >= "3.8" and python_version < "3.11"
+fsspec==2024.2.0 ; python_version >= "3.8" and python_version < "3.11"
 gast==0.4.0 ; python_version >= "3.8" and python_version < "3.11"
 google-auth-oauthlib==1.0.0 ; python_version >= "3.8" and python_version < "3.11"
-google-auth==2.23.2 ; python_version >= "3.8" and python_version < "3.11"
+google-auth==2.27.0 ; python_version >= "3.8" and python_version < "3.11"
 google-pasta==0.2.0 ; python_version >= "3.8" and python_version < "3.11"
-grpcio==1.59.0 ; python_version >= "3.8" and python_version < "3.11"
+grpcio==1.60.1 ; python_version >= "3.8" and python_version < "3.11"
 h11==0.14.0 ; python_version >= "3.8" and python_version < "3.11"
-h5py==3.9.0 ; python_version >= "3.8" and python_version < "3.11"
+h5py==3.10.0 ; python_version >= "3.8" and python_version < "3.11"
 httpcore==0.16.3 ; python_version >= "3.8" and python_version < "3.11"
 httpx==0.23.3 ; python_version >= "3.8" and python_version < "3.11"
-huggingface-hub==0.16.4 ; python_version >= "3.8" and python_version < "3.11"
-identify==2.5.30 ; python_version >= "3.8" and python_version < "3.11"
-idna==3.4 ; python_version >= "3.8" and python_version < "3.11"
-importlib-metadata==6.8.0 ; python_version >= "3.8" and python_version < "3.10"
-importlib-resources==6.1.0 ; python_version >= "3.8" and python_version < "3.10"
+huggingface-hub==0.20.3 ; python_version >= "3.8" and python_version < "3.11"
+identify==2.5.34 ; python_version >= "3.8" and python_version < "3.11"
+idna==3.6 ; python_version >= "3.8" and python_version < "3.11"
+importlib-metadata==7.0.1 ; python_version >= "3.8" and python_version < "3.10"
+importlib-resources==6.1.1 ; python_version >= "3.8" and python_version < "3.10"
 iniconfig==2.0.0 ; python_version >= "3.8" and python_version < "3.11"
 jax==0.4.13 ; python_version >= "3.8" and python_version < "3.11"
 joblib==1.3.2 ; python_version >= "3.8" and python_version < "3.11"
 keras==2.12.0 ; python_version >= "3.8" and python_version < "3.11"
 kiwisolver==1.4.5 ; python_version >= "3.8" and python_version < "3.11"
 libclang==16.0.6 ; python_version >= "3.8" and python_version < "3.11"
-markdown==3.4.4 ; python_version >= "3.8" and python_version < "3.11"
-markupsafe==2.1.3 ; python_version >= "3.8" and python_version < "3.11"
-matplotlib==3.7.3 ; python_version >= "3.8" and python_version < "3.11"
+markdown==3.5.2 ; python_version >= "3.8" and python_version < "3.11"
+markupsafe==2.1.5 ; python_version >= "3.8" and python_version < "3.11"
+matplotlib==3.7.4 ; python_version >= "3.8" and python_version < "3.11"
 ml-dtypes==0.2.0 ; python_version >= "3.8" and python_version < "3.11"
 nodeenv==1.8.0 ; python_version >= "3.8" and python_version < "3.11"
-numpy==1.23.5 ; python_version < "3.11" and python_version >= "3.8"
+numpy==1.23.5 ; python_version >= "3.8" and python_version < "3.11"
 oauthlib==3.2.2 ; python_version >= "3.8" and python_version < "3.11"
 opt-einsum==3.3.0 ; python_version >= "3.8" and python_version < "3.11"
 packaging==23.2 ; python_version >= "3.8" and python_version < "3.11"
 pandas==1.5.3 ; python_version >= "3.8" and python_version < "3.11"
-pillow==10.0.1 ; python_version >= "3.8" and python_version < "3.11"
-platformdirs==3.11.0 ; python_version >= "3.8" and python_version < "3.11"
-pluggy==1.3.0 ; python_version >= "3.8" and python_version < "3.11"
-pre-commit==3.4.0 ; python_version >= "3.8" and python_version < "3.11"
-protobuf==4.24.4 ; python_version >= "3.8" and python_version < "3.11"
+pillow==10.2.0 ; python_version >= "3.8" and python_version < "3.11"
+platformdirs==4.2.0 ; python_version >= "3.8" and python_version < "3.11"
+pluggy==1.4.0 ; python_version >= "3.8" and python_version < "3.11"
+pre-commit==3.5.0 ; python_version >= "3.8" and python_version < "3.11"
+protobuf==4.25.2 ; python_version >= "3.8" and python_version < "3.11"
 pyasn1-modules==0.3.0 ; python_version >= "3.8" and python_version < "3.11"
-pyasn1==0.5.0 ; python_version >= "3.8" and python_version < "3.11"
-pydantic==1.10.13 ; python_version >= "3.8" and python_version < "3.11"
+pyasn1==0.5.1 ; python_version >= "3.8" and python_version < "3.11"
+pydantic==1.10.14 ; python_version >= "3.8" and python_version < "3.11"
 pyparsing==3.1.1 ; python_version >= "3.8" and python_version < "3.11"
 pytest-cov==4.1.0 ; python_version >= "3.8" and python_version < "3.11"
-pytest-mock==3.11.1 ; python_version >= "3.8" and python_version < "3.11"
-pytest==7.4.2 ; python_version >= "3.8" and python_version < "3.11"
+pytest-mock==3.12.0 ; python_version >= "3.8" and python_version < "3.11"
+pytest==7.4.4 ; python_version >= "3.8" and python_version < "3.11"
 python-dateutil==2.8.2 ; python_version >= "3.8" and python_version < "3.11"
-pytz==2023.3.post1 ; python_version >= "3.8" and python_version < "3.11"
+pytz==2024.1 ; python_version >= "3.8" and python_version < "3.11"
 pyyaml==6.0.1 ; python_version >= "3.8" and python_version < "3.11"
-regex==2023.10.3 ; python_version >= "3.8" and python_version < "3.11"
+regex==2023.12.25 ; python_version >= "3.8" and python_version < "3.11"
 requests-oauthlib==1.3.1 ; python_version >= "3.8" and python_version < "3.11"
 requests==2.31.0 ; python_version >= "3.8" and python_version < "3.11"
 rfc3986[idna2008]==1.5.0 ; python_version >= "3.8" and python_version < "3.11"
 rsa==4.9 ; python_version >= "3.8" and python_version < "3.11"
 ruff==0.0.272 ; python_version >= "3.8" and python_version < "3.11"
-safetensors==0.3.3 ; python_version >= "3.8" and python_version < "3.11"
+safetensors==0.4.2 ; python_version >= "3.8" and python_version < "3.11"
 scikit-learn==1.0.2 ; python_version >= "3.8" and python_version < "3.11"
 scipy==1.10.1 ; python_version >= "3.8" and python_version < "3.11"
-setuptools-scm==8.0.4 ; python_version >= "3.8" and python_version < "3.11"
-setuptools==68.2.2 ; python_version >= "3.8" and python_version < "3.11"
+setuptools==69.1.0 ; python_version >= "3.8" and python_version < "3.11"
 six==1.16.0 ; python_version >= "3.8" and python_version < "3.11"
 sniffio==1.3.0 ; python_version >= "3.8" and python_version < "3.11"
 starlette==0.27.0 ; python_version >= "3.8" and python_version < "3.11"
-tensorboard-data-server==0.7.1 ; python_version >= "3.8" and python_version < "3.11"
+tensorboard-data-server==0.7.2 ; python_version >= "3.8" and python_version < "3.11"
 tensorboard==2.12.3 ; python_version >= "3.8" and python_version < "3.11"
 tensorflow-estimator==2.12.0 ; python_version >= "3.8" and python_version < "3.11"
-tensorflow-io-gcs-filesystem==0.34.0 ; python_version >= "3.8" and python_version < "3.11" and platform_machine != "arm64" or python_version >= "3.8" and python_version < "3.11" and platform_system != "Darwin"
+tensorflow-io-gcs-filesystem==0.36.0 ; python_version >= "3.8" and python_version < "3.11" and platform_machine != "arm64" or python_version >= "3.8" and python_version < "3.11" and platform_system != "Darwin"
 tensorflow==2.12.0 ; python_version >= "3.8" and python_version < "3.11"
-termcolor==2.3.0 ; python_version >= "3.8" and python_version < "3.11"
-threadpoolctl==3.2.0 ; python_version >= "3.8" and python_version < "3.11"
-tokenizers==0.14.0 ; python_version >= "3.8" and python_version < "3.11"
+termcolor==2.4.0 ; python_version >= "3.8" and python_version < "3.11"
+threadpoolctl==3.3.0 ; python_version >= "3.8" and python_version < "3.11"
+tokenizers==0.15.2 ; python_version >= "3.8" and python_version < "3.11"
 tomli==2.0.1 ; python_version >= "3.8" and python_version < "3.11"
-tornado==6.3.3 ; python_version >= "3.8" and python_version < "3.11"
-tqdm==4.66.1 ; python_version >= "3.8" and python_version < "3.11"
-transformers==4.34.0 ; python_version >= "3.8" and python_version < "3.11"
-typing-extensions==4.8.0 ; python_version >= "3.8" and python_version < "3.11"
-urllib3==2.0.6 ; python_version >= "3.8" and python_version < "3.11"
+tornado==6.4 ; python_version >= "3.8" and python_version < "3.11"
+tqdm==4.66.2 ; python_version >= "3.8" and python_version < "3.11"
+transformers==4.37.2 ; python_version >= "3.8" and python_version < "3.11"
+typing-extensions==4.9.0 ; python_version >= "3.8" and python_version < "3.11"
+urllib3==2.2.0 ; python_version >= "3.8" and python_version < "3.11"
 uvicorn==0.20.0 ; python_version >= "3.8" and python_version < "3.11"
-virtualenv==20.24.5 ; python_version >= "3.8" and python_version < "3.11"
-werkzeug==3.0.0 ; python_version >= "3.8" and python_version < "3.11"
-wheel==0.41.2 ; python_version >= "3.8" and python_version < "3.11"
+virtualenv==20.25.0 ; python_version >= "3.8" and python_version < "3.11"
+werkzeug==3.0.1 ; python_version >= "3.8" and python_version < "3.11"
+wheel==0.42.0 ; python_version >= "3.8" and python_version < "3.11"
 wrapt==1.14.1 ; python_version >= "3.8" and python_version < "3.11"
 xgboost==1.7.6 ; python_version >= "3.8" and python_version < "3.11"
 zipp==3.17.0 ; python_version >= "3.8" and python_version < "3.10"
-pxtextmining==1.0.0
diff --git a/datasets/README.md b/datasets/README.md
index eb20703f..68f0ff72 100644
--- a/datasets/README.md
+++ b/datasets/README.md
@@ -29,4 +29,4 @@ Person identifiable info?: Whether or not the FFT answer contains any person ide
 
 Comment sentiment: The sentiment score applied to the FFT answer by the labeller. 1 is "very positive", 5 is "very negative". Mixed comments have been labelled as "3", neutral.
 
-All other columns are the qualitative framework labels, in one hot encoded format. The version of the framework being used is reflected in the filename. Full details of the framework are available on the [project documentation website](https://cdu-data-science-team.github.io/PatientExperience-QDC/framework/framework3.html).
+All other columns are the qualitative framework labels, in one hot encoded format. The version of the framework being used is reflected in the filename. Full details of the framework are available on the [project documentation website](https://the-strategy-unit.github.io/PatientExperience-QDC/framework/framework3.html).
diff --git a/docker_README.md b/docker_README.md
index e87d3393..d61bbd78 100644
--- a/docker_README.md
+++ b/docker_README.md
@@ -1,6 +1,6 @@
 # pxtextmining: Text Classification of Patient Experience feedback
 
-This Docker container contains the pxtextmining machine learning models trained as part of the [Patient Experience Qualitative Data Categorisation project](https://cdu-data-science-team.github.io/PatientExperience-QDC/).
+This Docker container contains the pxtextmining machine learning models trained as part of the [Patient Experience Qualitative Data Categorisation project](https://the-strategy-unit.github.io/PatientExperience-QDC/).
 
 To use this Docker container to predict your unlabelled text:
 
@@ -87,7 +87,7 @@ docker_data/
    - `--target` or `-t` to select the machine learning models used. Options are `m` for multilabel, `s` for `sentiment`, or `ms` for both. Defaults to `ms` if nothing is selected.
 
 A sample command would be:
-`docker run --rm -it -v /docker_data:/data ghcr.io/cdu-data-science-team/pxtextmining:latest file_01.json -l `
+`docker run --rm -it -v /docker_data:/data ghcr.io/the-strategy-unit/pxtextmining:latest file_01.json -l `
 
 6. The predictions will be outputted as a json file in the data_out folder, with the same filename. After running successfully, the final folder structure should be:
 
diff --git a/docs/about.md b/docs/about.md
index dbc7a6a2..4e7e0415 100644
--- a/docs/about.md
+++ b/docs/about.md
@@ -1,7 +1,7 @@
 # Project background
 
-The `pxtextmining` package is part of [the Patient Experience Qualitative Data Categorisation project](https://cdu-data-science-team.github.io/PatientExperience-QDC/). This project is is hosted by Nottinghamshire Healthcare NHS Foundation Trust's Clinical Development Unit Data Science Team, and funded by NHS England's Insight and Feedback Team.
+The `pxtextmining` package is part of [the Patient Experience Qualitative Data Categorisation project](https://the-strategy-unit.github.io/PatientExperience-QDC/). This project is is hosted by Nottinghamshire Healthcare NHS Foundation Trust's Clinical Development Unit Data Science Team, and funded by NHS England's Insight and Feedback Team.
 
 The primary objective of the `pxtextmining` element is to create a machine learning model capable of categorising the free text data obtained through the [NHS England Friends and Family Test](https://www.england.nhs.uk/fft/) (FFT). It is a multilabel classification problem, with one or more categories applied to each patient feedback comment. In this way, we hope to support better use of qualitative patient experience feedback by NHS provider organisations.
 
-This package works together with the [experiencesdashboard](https://github.com/CDU-data-science-team/experiencesdashboard), a frontend coded in R/Shiny.
+This package works together with the [experiencesdashboard](https://github.com/the-strategy-unit/experiencesdashboard), a frontend coded in R/Shiny.
diff --git a/docs/getting started/install.md b/docs/getting started/install.md
index a86f4fa3..dcae2c99 100644
--- a/docs/getting started/install.md	
+++ b/docs/getting started/install.md	
@@ -1,6 +1,6 @@
 ## Installation
 
-You can install `pxtextmining` from either [PyPI](https://pypi.org/project/pxtextmining/) or [GitHub](https://github.com/CDU-data-science-team/pxtextmining).
+You can install `pxtextmining` from either [PyPI](https://pypi.org/project/pxtextmining/) or [GitHub](https://github.com/the-strategy-unit/pxtextmining).
 
 The recommended method is to clone the repository from GitHub, as this will also include the models and datasets.
 
diff --git a/docs/getting started/package.md b/docs/getting started/package.md
index 4c77595c..431d3e07 100644
--- a/docs/getting started/package.md	
+++ b/docs/getting started/package.md	
@@ -11,7 +11,7 @@ This module contains vast majority of the code in the package. There are five di
 
       - `factory_pipeline`: Construction and training of different models/estimators/algorithms using the `sklearn`, `tensorflow.keras` and `transformers` libraries.
 
-      - `factory_model_performance`: Evaluation of a trained model, comparing predicted targets with real target values, to produce performance metrics. The decision-making process behind the peformance metrics chosen can be seen on the [project documentation website](https://cdu-data-science-team.github.io/PatientExperience-QDC/pxtextmining/performance_metrics.html). The performance metrics for the current best models utilised in the API can be found in the `current_best_multilabel` folder in the main repository.
+      - `factory_model_performance`: Evaluation of a trained model, comparing predicted targets with real target values, to produce performance metrics. The decision-making process behind the peformance metrics chosen can be seen on the [project documentation website](https://the-strategy-unit.github.io/PatientExperience-QDC/pxtextmining/performance_metrics.html). The performance metrics for the current best models utilised in the API can be found in the `current_best_multilabel` folder in the main repository.
 
       - `factory_predict_unlabelled_text`: Prepares unlabelled text (with or without additional features such as question type) in a format suitable for each model type, and passes this through the selected models, to produce predicted labels.
 
diff --git a/docs/index.md b/docs/index.md
index 21379a83..61d88002 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -2,7 +2,7 @@
 
 This site contains the project documentation for the `pxtextmining` python package.
 This provides a technical overview of the package; for a non-technical overview and further information, visit the
-[Patient Experience Qualitative Data Categorisation website](https://cdu-data-science-team.github.io/PatientExperience-QDC/pxtextmining/).
+[Patient Experience Qualitative Data Categorisation website](https://the-strategy-unit.github.io/PatientExperience-QDC/pxtextmining/).
 
 ## Table Of Contents
 
diff --git a/docs/reference/API/API.md b/docs/reference/API/API.md
index bbe33017..a5bc2083 100644
--- a/docs/reference/API/API.md
+++ b/docs/reference/API/API.md
@@ -1,6 +1,6 @@
-# pxtextmining APIs
+# pxtextmining API overview
 
-We have created two different APIs for labelling patient experience feedback. Both APIs are free to use and completely open source. For help and support with using them, please contact (Chris Beeley)[chris.beeley1@nhs.net].
+We have created two different APIs for labelling patient experience feedback. Both APIs are free to use and completely open source. For help and support with using them, please contact [Chris Beeley](mailto:chris.beeley1@nhs.net).
 
 The "Quick API" is faster and simpler, as it uses an sklearn model which is quicker to make predictions. The performance of predictions from this API can be seen on our project documentation website. It is less accurate than the slow API. This API is a more 'traditional' style of API.
 
diff --git a/docs/reference/API/quick_API.md b/docs/reference/API/quick_API.md
index c73ef7c5..0a6d0571 100644
--- a/docs/reference/API/quick_API.md
+++ b/docs/reference/API/quick_API.md
@@ -2,7 +2,7 @@
 
 To facilitate the use of the models trained in this project, an API has been created using the FastAPI library. Users will be able to send their patient experience feedback comments to the model via the API, and will receive the predicted labels for those comments.
 
-This API utilises the Support Vector Classifier model which is less performant than the transformer-based Distilbert model. However, it is also much quicker and simpler. Performance metrics for this model can be seen on our [project documentation website](https://cdu-data-science-team.github.io/PatientExperience-QDC/).
+This API utilises the Support Vector Classifier model which is less performant than the transformer-based Distilbert model. However, it is also much quicker and simpler. Performance metrics for this model can be seen on our [project documentation website](https://the-strategy-unit.github.io/PatientExperience-QDC/).
 
 The API has been created using FastAPI and is deployed on Posit Connect. The URL is available on request. Full documentation for the API, automatically generated by FastAPI, is available at [API URL]/docs.
 
diff --git a/docs/reference/API/slow_API.md b/docs/reference/API/slow_API.md
index 033d2a43..21572eda 100644
--- a/docs/reference/API/slow_API.md
+++ b/docs/reference/API/slow_API.md
@@ -1,3 +1,146 @@
 # Slow API
 
-Details to follow shortly!
+This API is slower but uses the best performing models. The transformer-based Distilbert model consumes a lot of hardware resource, and as such required a different approach.
+
+![Diagram showing Slow API architecture](https://the-strategy-unit.github.io/PatientExperience-QDC/pxtextmining/slow_API.png)
+
+For predicting the multilabel categories, the API endpoint ensembles together Support Vector Classifier, Gradient Boosted Decision Trees (XGBoost), and Distilbert models.
+
+For predicting text sentiment , the API endpoint utilises a Distilbert model.
+
+The API URL endpoint is available on request. You will need an API key, please contact the project team to obtain one. The key should be passed as a `code` param with your API request.
+
+## How to make an API call
+
+1\. Prepare the data in JSON format. In Python, this is a `list` containing as many `dict`s as there are comments to be predicted. Each `dict` has three compulsory keys:
+
+  * `comment_id`: Unique ID associated with the comment, in `str` format. Each Comment ID per API call must be unique.
+  * `comment_text`: Text to be classified, in `str` format.
+  * `question_type`: The type of question asked to elicit the comment text. Questions are different from trust to trust, but they all fall into one of three categories:
+       * `what_good`: Any variation on the question "What was good about the service?", or "What did we do well?"
+       * `could_improve`: Any variation on the question "Please tell us about anything that we could have done better", or "How could we improve?"
+       * `nonspecific`: Any other type of nonspecific question, e.g. "Please can you tell us why you gave your answer?", or "What were you satisfied and/or dissatisfied with?".
+
+```python
+# In Python
+
+text_data = [
+              { 'comment_id': '1', # The comment_id values in each dict must be unique.
+                'comment_text': 'This is the first comment. Nurse was great.',
+                'question_type': 'what_good' },
+              { 'comment_id': '2',
+                'comment_text': 'This is the second comment. The ward was freezing.',
+                'question_type': 'could_improve' },
+              { 'comment_id': '3',
+                'comment_text': '',  # This comment is an empty string.
+                'question_type': 'nonspecific' }
+            ]
+```
+
+```R
+# In R
+
+library(jsonlite)
+
+comment_id <- c("1", "2", "3")
+comment_text <- c(
+  "This is the first comment. Nurse was great.",
+  "This is the second comment. The ward was freezing.",
+  ""
+)
+question_type <- c("what_good", "could_improve", "nonspecific")
+df <- data.frame(comment_id, comment_text, question_type)
+text_data <- toJSON(df)
+```
+
+2\. Send the JSON containing the text data in a POST request to the API. Ensure that you include your API key, which should be stored securely.
+
+The model(s) used to make predictions can be selected with the `target` param. The options for this param are:
+
+- `m`: multilabel
+- `s`: sentiment
+- `ms`: both multilabel and sentiment.
+
+```python
+# In Python
+
+api_key = os.getenv('API_KEY')
+params_dict = {'code': api_key, 'target': 'ms'}
+
+url = os.getenv('API_URL')
+
+response = requests.post(url, params= params_dict, json = text_data)
+```
+
+```R
+# In R
+library(httr)
+
+api_key <- Sys.getenv("API_KEY")
+params_dict <- list(code = api_key, target = "ms")
+url <- Sys.getenv("API_URL")
+
+response <- POST(url, query = params_dict, body = text_data, encode = "json")
+```
+
+3\. If the POST request is successful, you will receive a response with a 202 code, and a URL to retrieve your results, called the `results URL`. For example:
+
+```python
+# In Python
+
+if response.status_code == 202:
+    results_url = response.text
+
+    print(f"URL for results is {results_url}")
+```
+
+```R
+# In R
+
+if (http_status(response) == 202) {
+    results_url <- content(response, as = "text")
+    }
+    print(results_url)
+```
+
+4\. Use a GET request to check the results URL. If your predictions are not yet ready, you will receive a 202 response. If they are ready, you will receive a 200 response.
+
+What is happening behind the scenes? The API has received your data and has started up a secure Azure container instance with your data stored in blob storage. The Docker container will install the pxtextmining package and make predictions using your data. Starting up a fresh container instance can take up to 5 minutes, and predictions using the slow transformer models can some time, up to 5 further minutes per 1000 comments. Once the predictions are complete, it will delete your data and save the predictions in blob storage.
+
+Once you receive a 200 response, your results are available in JSON format. Please note that this will only be available once; once you have collected the data, it will be deleted due to security reasons and your results URL will no longer be valid.
+
+You can set up a loop to check if your results are ready every 5 minutes, as follows.
+
+```python
+# In Python
+
+while True:
+    results_response = requests.get(results_url)
+    if results_response.status_code == 200:
+        final_labels = results_response.json()
+        break
+    else:
+        print('Not ready! Trying again in 300 seconds...')
+        time.sleep(300)
+
+print('Predicted labels':)
+print(final_labels)
+```
+
+```R
+# In R
+
+while (TRUE) {
+  results_response <- GET(results_url)
+  if (results_response$status_code == 200) {
+    final_labels <- fromJSON(content(results_response, "text"))
+    break
+  } else {
+    cat("Not ready! Trying again in 300 seconds...\n")
+    Sys.sleep(300)
+  }
+}
+
+cat("Predicted labels:\n")
+print(final_labels)
+```
diff --git a/docs/reference/Docker/docker_README.md b/docs/reference/Docker/docker_README.md
new file mode 100644
index 00000000..43290722
--- /dev/null
+++ b/docs/reference/Docker/docker_README.md
@@ -0,0 +1,101 @@
+# Using our Docker container
+
+This Docker container contains the pxtextmining machine learning models trained as part of the [Patient Experience Qualitative Data Categorisation project](https://the-strategy-unit.github.io/PatientExperience-QDC/).
+
+To use this Docker container to predict your unlabelled text:
+
+1\. Set up your folders. You will need to set up a folder containing two other folders, data_in and data_out, as below.
+```
+docker_data/
+├─ data_in/
+├─ data_out/
+
+```
+
+2\. Prepare your data. Save the data you wish to pass through the machine learning models as json, in the data_in folder. The data should be in the following format:
+
+In Python, a `list` containing as many `dict`s as there are comments to be predicted. Each `dict` has three compulsory keys:
+
+  * `comment_id`: Unique ID associated with the comment, in `str` format. Each Comment ID per API call must be unique.
+  * `comment_text`: Text to be classified, in `str` format.
+  * `question_type`: The type of question asked to elicit the comment text. Questions are different from trust to trust, but they all fall into one of three categories:
+       * `what_good`: Any variation on the question "What was good about the service?", or "What did we do well?"
+       * `could_improve`: Any variation on the question "Please tell us about anything that we could have done better", or "How could we improve?"
+       * `nonspecific`: Any other type of nonspecific question, e.g. "Please can you tell us why you gave your answer?", or "What were you satisfied and/or dissatisfied with?".
+
+```python
+# In Python
+
+text_data = [
+              { 'comment_id': '1', # The comment_id values in each dict must be unique.
+                'comment_text': 'This is the first comment. Nurse was great.',
+                'question_type': 'what_good' },
+              { 'comment_id': '2',
+                'comment_text': 'This is the second comment. The ward was freezing.',
+                'question_type': 'could_improve' },
+              { 'comment_id': '3',
+                'comment_text': '',  # This comment is an empty string.
+                'question_type': 'nonspecific' }
+            ]
+
+```
+
+```R
+# In R
+
+library(jsonlite)
+
+comment_id <- c("1", "2", "3")
+comment_text <- c(
+  "This is the first comment. Nurse was great.",
+  "This is the second comment. The ward was freezing.",
+  ""
+)
+question_type <- c("what_good", "could_improve", "nonspecific")
+df <- data.frame(comment_id, comment_text, question_type)
+text_data <- toJSON(df)
+```
+
+3\. Save the JSON data in the data_in folder, as follows:
+
+```python
+# In Python
+
+json_data = json.dumps(text_data)
+with open("data_in/file_01.json", "w") as outfile:
+    outfile.write(json_data)
+```
+
+```R
+# In R
+
+json_data <- toJSON(text_data, pretty = TRUE)
+write(json_data, file = "data_in/file_01.json")
+```
+
+4\. Your file structure should now look like this:
+
+```
+docker_data/
+├─ data_in/
+│  ├─ file_01.json
+├─ data_out/
+```
+
+5\. Mount the docker_data folder as the `data` volume for the Docker container and run the container. Pass the filename for the input JSON as the first argument. The following arguments are also available:
+
+   - `--local-storage` or `-l` flag for local storage (does not delete the files in data_in after completing predictions)
+   - `--target` or `-t` to select the machine learning models used. Options are `m` for multilabel, `s` for `sentiment`, or `ms` for both. Defaults to `ms` if nothing is selected.
+
+A sample command would be:
+`docker run --rm -it -v /docker_data:/data ghcr.io/the-strategy-unit/pxtextmining:latest file_01.json -l `
+
+6\. The predictions will be outputted as a json file in the data_out folder, with the same filename. After running successfully, the final folder structure should be:
+
+```
+docker_data/
+├─ data_in/
+│  ├─ file_01.json
+├─ data_out/
+   ├─ file_01.json
+```
diff --git a/docs/reference/pxtextmining/helpers/tokenization.md b/docs/reference/pxtextmining/helpers/tokenization.md
deleted file mode 100644
index 3c616e5c..00000000
--- a/docs/reference/pxtextmining/helpers/tokenization.md
+++ /dev/null
@@ -1,3 +0,0 @@
-::: pxtextmining.helpers.tokenization
-    options:
-        show_source: true
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
index 6e02e76f..47de7cd9 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -1,5 +1,5 @@
 site_name: pxtextmining
-site_url: https://cdu-data-science-team.github.io/pxtextmining/
+site_url: https://the-strategy-unit.github.io/pxtextmining/
 theme: readthedocs
 watch:
 - pxtextmining
diff --git a/poetry.lock b/poetry.lock
index b64e770b..6cc85771 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,36 +1,48 @@
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+
 [[package]]
 name = "absl-py"
-version = "2.0.0"
+version = "2.1.0"
 description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
-category = "main"
 optional = false
 python-versions = ">=3.7"
+files = [
+    {file = "absl-py-2.1.0.tar.gz", hash = "sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff"},
+    {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"},
+]
 
 [[package]]
 name = "anyio"
-version = "4.0.0"
+version = "4.2.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
+files = [
+    {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"},
+    {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"},
+]
 
 [package.dependencies]
 exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
-trio = ["trio (>=0.22)"]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
 
 [[package]]
 name = "astunparse"
 version = "1.6.3"
 description = "An AST unparser for Python"
-category = "main"
 optional = false
 python-versions = "*"
+files = [
+    {file = "astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8"},
+    {file = "astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872"},
+]
 
 [package.dependencies]
 six = ">=1.6.1,<2.0"
@@ -38,43 +50,146 @@ wheel = ">=0.23.0,<1.0"
 
 [[package]]
 name = "cachetools"
-version = "5.3.1"
+version = "5.3.2"
 description = "Extensible memoizing collections and decorators"
-category = "main"
 optional = false
 python-versions = ">=3.7"
+files = [
+    {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"},
+    {file = "cachetools-5.3.2.tar.gz", hash = "sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2"},
+]
 
 [[package]]
 name = "certifi"
-version = "2023.7.22"
+version = "2024.2.2"
 description = "Python package for providing Mozilla's CA Bundle."
-category = "main"
 optional = false
 python-versions = ">=3.6"
+files = [
+    {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
+    {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
+]
 
 [[package]]
 name = "cfgv"
 version = "3.4.0"
 description = "Validate configuration and produce human readable error messages."
-category = "dev"
 optional = false
 python-versions = ">=3.8"
+files = [
+    {file = "cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9"},
+    {file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"},
+]
 
 [[package]]
 name = "charset-normalizer"
-version = "3.3.0"
+version = "3.3.2"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
-category = "main"
 optional = false
 python-versions = ">=3.7.0"
+files = [
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 [[package]]
 name = "click"
 version = "8.1.7"
 description = "Composable command line interface toolkit"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
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 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
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 name = "colorama"
 version = "0.4.6"
 description = "Cross-platform colored terminal text."
-category = "main"
 optional = false
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 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
 numpy = {version = ">=1.16,<2.0", markers = "python_version <= \"3.11\""}
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+]
 
 [package.dependencies]
 tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.11.0a6\" and extra == \"toml\""}
@@ -121,11 +345,14 @@ toml = ["tomli"]
 
 [[package]]
 name = "cycler"
-version = "0.12.0"
+version = "0.12.1"
 description = "Composable style cycles"
-category = "main"
 optional = false
 python-versions = ">=3.8"
+files = [
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+]
 
 [package.extras]
 docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
@@ -133,19 +360,25 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "distlib"
-version = "0.3.7"
+version = "0.3.8"
 description = "Distribution utilities"
-category = "dev"
 optional = false
 python-versions = "*"
+files = [
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+    {file = "distlib-0.3.8.tar.gz", hash = "sha256:1530ea13e350031b6312d8580ddb6b27a104275a31106523b8f123787f494f64"},
+]
 
 [[package]]
 name = "exceptiongroup"
-version = "1.1.3"
+version = "1.2.0"
 description = "Backport of PEP 654 (exception groups)"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
+files = [
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+    {file = "exceptiongroup-1.2.0.tar.gz", hash = "sha256:91f5c769735f051a4290d52edd0858999b57e5876e9f85937691bd4c9fa3ed68"},
+]
 
 [package.extras]
 test = ["pytest (>=6)"]
@@ -154,9 +387,12 @@ test = ["pytest (>=6)"]
 name = "fastapi"
 version = "0.101.1"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
+files = [
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+    {file = "fastapi-0.101.1.tar.gz", hash = "sha256:7b32000d14ca9992f7461117b81e4ef9ff0c07936af641b4fe40e67d5f9d63cb"},
+]
 
 [package.dependencies]
 pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
@@ -168,54 +404,106 @@ all = ["email-validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)"
 
 [[package]]
 name = "filelock"
-version = "3.12.4"
+version = "3.13.1"
 description = "A platform independent file lock."
-category = "main"
 optional = false
 python-versions = ">=3.8"
+files = [
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+    {file = "filelock-3.13.1.tar.gz", hash = "sha256:521f5f56c50f8426f5e03ad3b281b490a87ef15bc6c526f168290f0c7148d44e"},
+]
 
 [package.extras]
-docs = ["furo (>=2023.7.26)", "sphinx (>=7.1.2)", "sphinx-autodoc-typehints (>=1.24)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.3)", "diff-cover (>=7.7)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)", "pytest-timeout (>=2.1)"]
-typing = ["typing-extensions (>=4.7.1)"]
+docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
+typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
 name = "flatbuffers"
 version = "23.5.26"
 description = "The FlatBuffers serialization format for Python"
-category = "main"
 optional = false
 python-versions = "*"
+files = [
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+]
 
 [[package]]
 name = "fonttools"
-version = "4.43.0"
+version = "4.48.1"
 description = "Tools to manipulate font files"
-category = "main"
 optional = false
 python-versions = ">=3.8"
+files = [
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+    {file = "fonttools-4.48.1.tar.gz", hash = "sha256:8b8a45254218679c7f1127812761e7854ed5c8e34349aebf581e8c9204e7495a"},
+]
 
 [package.extras]
-all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.0.0)", "xattr", "zopfli (>=0.1.4)"]
+all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "pycairo", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0)", "xattr", "zopfli (>=0.1.4)"]
 graphite = ["lz4 (>=1.7.4.2)"]
-interpolatable = ["munkres", "scipy"]
-lxml = ["lxml (>=4.0,<5)"]
+interpolatable = ["munkres", "pycairo", "scipy"]
+lxml = ["lxml (>=4.0)"]
 pathops = ["skia-pathops (>=0.5.0)"]
 plot = ["matplotlib"]
 repacker = ["uharfbuzz (>=0.23.0)"]
 symfont = ["sympy"]
 type1 = ["xattr"]
 ufo = ["fs (>=2.2.0,<3)"]
-unicode = ["unicodedata2 (>=15.0.0)"]
+unicode = ["unicodedata2 (>=15.1.0)"]
 woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
 [[package]]
 name = "fsspec"
-version = "2023.9.2"
+version = "2024.2.0"
 description = "File-system specification"
-category = "main"
 optional = false
 python-versions = ">=3.8"
+files = [
+    {file = "fsspec-2024.2.0-py3-none-any.whl", hash = "sha256:817f969556fa5916bc682e02ca2045f96ff7f586d45110fcb76022063ad2c7d8"},
+    {file = "fsspec-2024.2.0.tar.gz", hash = "sha256:b6ad1a679f760dda52b1168c859d01b7b80648ea6f7f7c7f5a8a91dc3f3ecb84"},
+]
 
 [package.extras]
 abfs = ["adlfs"]
@@ -232,7 +520,7 @@ github = ["requests"]
 gs = ["gcsfs"]
 gui = ["panel"]
 hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
 libarchive = ["libarchive-c"]
 oci = ["ocifs"]
 s3 = ["s3fs"]
@@ -245,17 +533,23 @@ tqdm = ["tqdm"]
 name = "gast"
 version = "0.4.0"
 description = "Python AST that abstracts the underlying Python version"
-category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+    {file = "gast-0.4.0-py3-none-any.whl", hash = "sha256:b7adcdd5adbebf1adf17378da5ba3f543684dbec47b1cda1f3997e573cd542c4"},
+    {file = "gast-0.4.0.tar.gz", hash = "sha256:40feb7b8b8434785585ab224d1568b857edb18297e5a3047f1ba012bc83b42c1"},
+]
 
 [[package]]
 name = "ghp-import"
 version = "2.1.0"
 description = "Copy your docs directly to the gh-pages branch."
-category = "dev"
 optional = false
 python-versions = "*"
+files = [
+    {file = "ghp-import-2.1.0.tar.gz", hash = "sha256:9c535c4c61193c2df8871222567d7fd7e5014d835f97dc7b7439069e2413d343"},
+    {file = "ghp_import-2.1.0-py3-none-any.whl", hash = "sha256:8337dd7b50877f163d4c0289bc1f1c7f127550241988d568c1db512c4324a619"},
+]
 
 [package.dependencies]
 python-dateutil = ">=2.8.1"
@@ -265,11 +559,14 @@ dev = ["flake8", "markdown", "twine", "wheel"]
 
 [[package]]
 name = "google-auth"
-version = "2.23.2"
+version = "2.27.0"
 description = "Google Authentication Library"
-category = "main"
 optional = false
 python-versions = ">=3.7"
+files = [
+    {file = "google-auth-2.27.0.tar.gz", hash = "sha256:e863a56ccc2d8efa83df7a80272601e43487fa9a728a376205c86c26aaefa821"},
+    {file = "google_auth-2.27.0-py2.py3-none-any.whl", hash = "sha256:8e4bad367015430ff253fe49d500fdc3396c1a434db5740828c728e45bcce245"},
+]
 
 [package.dependencies]
 cachetools = ">=2.0.0,<6.0"
@@ -287,9 +584,12 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 name = "google-auth-oauthlib"
 version = "1.0.0"
 description = "Google Authentication Library"
-category = "main"
 optional = false
 python-versions = ">=3.6"
+files = [
+    {file = "google-auth-oauthlib-1.0.0.tar.gz", hash = "sha256:e375064964820b47221a7e1b7ee1fd77051b6323c3f9e3e19785f78ab67ecfc5"},
+    {file = "google_auth_oauthlib-1.0.0-py2.py3-none-any.whl", hash = "sha256:95880ca704928c300f48194d1770cf5b1462835b6e49db61445a520f793fd5fb"},
+]
 
 [package.dependencies]
 google-auth = ">=2.15.0"
@@ -302,50 +602,141 @@ tool = ["click (>=6.0.0)"]
 name = "google-pasta"
 version = "0.2.0"
 description = "pasta is an AST-based Python refactoring library"
-category = "main"
 optional = false
 python-versions = "*"
+files = [
+    {file = "google-pasta-0.2.0.tar.gz", hash = "sha256:c9f2c8dfc8f96d0d5808299920721be30c9eec37f2389f28904f454565c8a16e"},
+    {file = "google_pasta-0.2.0-py2-none-any.whl", hash = "sha256:4612951da876b1a10fe3960d7226f0c7682cf901e16ac06e473b267a5afa8954"},
+    {file = "google_pasta-0.2.0-py3-none-any.whl", hash = "sha256:b32482794a366b5366a32c92a9a9201b107821889935a02b3e51f6b432ea84ed"},
+]
 
 [package.dependencies]
 six = "*"
 
 [[package]]
 name = "griffe"
-version = "0.36.4"
+version = "0.40.1"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
-category = "dev"
 optional = false
 python-versions = ">=3.8"
+files = [
+    {file = "griffe-0.40.1-py3-none-any.whl", hash = "sha256:5b8c023f366fe273e762131fe4bfd141ea56c09b3cb825aa92d06a82681cfd93"},
+    {file = "griffe-0.40.1.tar.gz", hash = "sha256:66c48a62e2ce5784b6940e603300fcfb807b6f099b94e7f753f1841661fd5c7c"},
+]
 
 [package.dependencies]
 colorama = ">=0.4"
 
 [[package]]
 name = "grpcio"
-version = "1.59.0"
+version = "1.60.1"
 description = "HTTP/2-based RPC framework"
-category = "main"
 optional = false
 python-versions = ">=3.7"
+files = [
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 [package.dependencies]
 numpy = ">=1.17.3"
@@ -354,27 +745,33 @@ numpy = ">=1.17.3"
 name = "httpcore"
 version = "0.16.3"
 description = "A minimal low-level HTTP client."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
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 certifi = "*"
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-sniffio = ">=1.0.0,<2.0.0"
+sniffio = "==1.*"
 
 [package.extras]
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+socks = ["socksio (==1.*)"]
 
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 name = "httpx"
 version = "0.23.3"
 description = "The next generation HTTP client."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
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 [package.dependencies]
 certifi = "*"
@@ -384,21 +781,24 @@ sniffio = "*"
 
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-cli = ["click (>=8.0.0,<9.0.0)", "pygments (>=2.0.0,<3.0.0)", "rich (>=10,<13)"]
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 http2 = ["h2 (>=3,<5)"]
-socks = ["socksio (>=1.0.0,<2.0.0)"]
+socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.16.4"
+version = "0.20.3"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
-category = "main"
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.8.0"
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 [package.dependencies]
 filelock = "*"
-fsspec = "*"
+fsspec = ">=2023.5.0"
 packaging = ">=20.9"
 pyyaml = ">=5.1"
 requests = "*"
@@ -406,59 +806,71 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
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-inference = ["aiohttp", "pydantic"]
-quality = ["black (>=23.1,<24.0)", "mypy (==0.982)", "ruff (>=0.0.241)"]
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 tensorflow = ["graphviz", "pydot", "tensorflow"]
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 torch = ["torch"]
-typing = ["pydantic", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
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 name = "identify"
-version = "2.5.30"
+version = "2.5.34"
 description = "File identification library for Python"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
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+]
 
 [package.extras]
 license = ["ukkonen"]
 
 [[package]]
 name = "idna"
-version = "3.4"
+version = "3.6"
 description = "Internationalized Domain Names in Applications (IDNA)"
-category = "main"
 optional = false
 python-versions = ">=3.5"
+files = [
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+]
 
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 name = "importlib-metadata"
-version = "6.8.0"
+version = "7.0.1"
 description = "Read metadata from Python packages"
-category = "main"
 optional = false
 python-versions = ">=3.8"
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+]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
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 perf = ["ipython"]
 testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
 
 [[package]]
 name = "importlib-resources"
-version = "6.1.0"
+version = "6.1.1"
 description = "Read resources from Python packages"
-category = "main"
 optional = false
 python-versions = ">=3.8"
+files = [
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+]
 
 [package.dependencies]
 zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
@@ -471,17 +883,22 @@ testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)",
 name = "iniconfig"
 version = "2.0.0"
 description = "brain-dead simple config-ini parsing"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
+files = [
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+]
 
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 name = "jax"
 version = "0.4.13"
 description = "Differentiate, compile, and transform Numpy code."
-category = "main"
 optional = false
 python-versions = ">=3.8"
+files = [
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+]
 
 [package.dependencies]
 importlib_metadata = {version = ">=4.6", markers = "python_version < \"3.10\""}
@@ -505,11 +922,14 @@ tpu = ["jaxlib (==0.4.13)", "libtpu-nightly (==0.1.dev20230622)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.2"
+version = "3.1.3"
 description = "A very fast and expressive template engine."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
+files = [
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+]
 
 [package.dependencies]
 MarkupSafe = ">=2.0"
@@ -521,64 +941,298 @@ i18n = ["Babel (>=2.7)"]
 name = "joblib"
 version = "1.3.2"
 description = "Lightweight pipelining with Python functions"
-category = "main"
 optional = false
 python-versions = ">=3.7"
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+]
 
 [[package]]
 name = "keras"
 version = "2.12.0"
 description = "Deep learning for humans."
-category = "main"
 optional = false
 python-versions = ">=3.8"
+files = [
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+]
 
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 name = "kiwisolver"
 version = "1.4.5"
 description = "A fast implementation of the Cassowary constraint solver"
-category = "main"
 optional = false
 python-versions = ">=3.7"
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 [package.dependencies]
 contourpy = ">=1.0.1"
@@ -591,23 +1245,28 @@ packaging = ">=20.0"
 pillow = ">=6.2.0"
 pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
-setuptools_scm = ">=7"
 
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 version = "1.3.4"
 description = "A deep merge function for 🐍."
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 optional = false
 python-versions = ">=3.6"
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+]
+
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 name = "mkdocs"
 version = "1.5.3"
 description = "Project documentation with Markdown."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
+files = [
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+]
 
 [package.dependencies]
 click = ">=7.0"
@@ -633,9 +1292,12 @@ min-versions = ["babel (==2.9.0)", "click (==7.0)", "colorama (==0.4)", "ghp-imp
 name = "mkdocs-autorefs"
 version = "0.5.0"
 description = "Automatically link across pages in MkDocs."
-category = "dev"
 optional = false
 python-versions = ">=3.8"
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+]
 
 [package.dependencies]
 Markdown = ">=3.3"
@@ -645,9 +1307,12 @@ mkdocs = ">=1.1"
 name = "mkdocstrings"
 version = "0.19.1"
 description = "Automatic documentation from sources, for MkDocs."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
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+]
 
 [package.dependencies]
 Jinja2 = ">=2.11.1"
@@ -666,9 +1331,12 @@ python-legacy = ["mkdocstrings-python-legacy (>=0.2.1)"]
 name = "mkdocstrings-python"
 version = "0.8.3"
 description = "A Python handler for mkdocstrings."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
+files = [
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+]
 
 [package.dependencies]
 griffe = ">=0.24"
@@ -678,14 +1346,32 @@ mkdocstrings = ">=0.19"
 name = "ml-dtypes"
 version = "0.2.0"
 description = ""
-category = "main"
 optional = false
 python-versions = ">=3.7"
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 [package.dependencies]
 numpy = [
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     {version = ">=1.21.2", markers = "python_version > \"3.9\""},
+    {version = ">1.20", markers = "python_version <= \"3.9\""},
 ]
 
 [package.extras]
@@ -695,9 +1381,12 @@ dev = ["absl-py", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-xdist"]
 name = "nodeenv"
 version = "1.8.0"
 description = "Node.js virtual environment builder"
-category = "dev"
 optional = false
 python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*"
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+]
 
 [package.dependencies]
 setuptools = "*"
@@ -706,17 +1395,49 @@ setuptools = "*"
 name = "numpy"
 version = "1.23.5"
 description = "NumPy is the fundamental package for array computing with Python."
-category = "main"
 optional = false
 python-versions = ">=3.8"
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+]
 
 [[package]]
 name = "oauthlib"
 version = "3.2.2"
 description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic"
-category = "main"
 optional = false
 python-versions = ">=3.6"
+files = [
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+]
 
 [package.extras]
 rsa = ["cryptography (>=3.0.0)"]
@@ -727,9 +1448,12 @@ signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
 name = "opt-einsum"
 version = "3.3.0"
 description = "Optimizing numpys einsum function"
-category = "main"
 optional = false
 python-versions = ">=3.5"
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 [package.dependencies]
 numpy = ">=1.7"
@@ -742,17 +1466,48 @@ tests = ["pytest", "pytest-cov", "pytest-pep8"]
 name = "packaging"
 version = "23.2"
 description = "Core utilities for Python packages"
-category = "main"
 optional = false
 python-versions = ">=3.7"
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 version = "1.5.3"
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 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
 numpy = [
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 [[package]]
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 [package.extras]
 docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
+fpx = ["olefile"]
+mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+typing = ["typing-extensions"]
+xmp = ["defusedxml"]
 
 [[package]]
 name = "platformdirs"
-version = "3.11.0"
+version = "4.2.0"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
-category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
+files = [
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 [package.extras]
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-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)"]
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+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
 
 [[package]]
 name = "pluggy"
-version = "1.3.0"
+version = "1.4.0"
 description = "plugin and hook calling mechanisms for python"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
+files = [
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+]
 
 [package.extras]
 dev = ["pre-commit", "tox"]
@@ -811,11 +1648,14 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "pre-commit"
-version = "3.4.0"
+version = "3.5.0"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
-category = "dev"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
 cfgv = ">=2.0.0"
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 description = ""
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+]
 
 [package.dependencies]
 typing-extensions = ">=4.2.0"
@@ -868,14 +1763,17 @@ email = ["email-validator (>=1.0.3)"]
 
 [[package]]
 name = "pymdown-extensions"
-version = "10.3"
+version = "10.7"
 description = "Extension pack for Python Markdown."
-category = "dev"
 optional = false
 python-versions = ">=3.8"
+files = [
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+]
 
 [package.dependencies]
-markdown = ">=3.2"
+markdown = ">=3.5"
 pyyaml = "*"
 
 [package.extras]
@@ -885,20 +1783,26 @@ extra = ["pygments (>=2.12)"]
 name = "pyparsing"
 version = "3.1.1"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
-category = "main"
 optional = false
 python-versions = ">=3.6.8"
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 [package.extras]
 diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "7.4.2"
+version = "7.4.4"
 description = "pytest: simple powerful testing with Python"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
+files = [
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+]
 
 [package.dependencies]
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
@@ -915,9 +1819,12 @@ testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "no
 name = "pytest-cov"
 version = "4.1.0"
 description = "Pytest plugin for measuring coverage."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
+files = [
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+]
 
 [package.dependencies]
 coverage = {version = ">=5.2.1", extras = ["toml"]}
@@ -928,11 +1835,14 @@ testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtuale
 
 [[package]]
 name = "pytest-mock"
-version = "3.11.1"
+version = "3.12.0"
 description = "Thin-wrapper around the mock package for easier use with pytest"
-category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
+files = [
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+]
 
 [package.dependencies]
 pytest = ">=5.0"
@@ -944,55 +1854,213 @@ dev = ["pre-commit", "pytest-asyncio", "tox"]
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 description = "Extensions to the standard Python datetime module"
-category = "main"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
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+]
 
 [package.dependencies]
 six = ">=1.5"
 
 [[package]]
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-version = "2023.3.post1"
+version = "2024.1"
 description = "World timezone definitions, modern and historical"
-category = "main"
 optional = false
 python-versions = "*"
+files = [
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+]
 
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 name = "pyyaml"
 version = "6.0.1"
 description = "YAML parser and emitter for Python"
-category = "main"
 optional = false
 python-versions = ">=3.6"
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 [package.extras]
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 python-versions = ">= 3.8"
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-category = "main"
 optional = false
 python-versions = ">=3.7"
+files = [
+    {file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"},
+    {file = "tqdm-4.66.2.tar.gz", hash = "sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531"},
+]
 
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
@@ -1326,50 +2755,52 @@ telegram = ["requests"]
 
 [[package]]
 name = "transformers"
-version = "4.34.0"
+version = "4.37.2"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
-category = "main"
 optional = false
 python-versions = ">=3.8.0"
+files = [
+    {file = "transformers-4.37.2-py3-none-any.whl", hash = "sha256:595a8b12a1fcc4ad0ced49ce206c58e17be68c85d7aee3d7546d04a32c910d2e"},
+    {file = "transformers-4.37.2.tar.gz", hash = "sha256:f307082ae5d528b8480611a4879a4a11651012d0e9aaea3f6cf17219ffd95542"},
+]
 
 [package.dependencies]
 filelock = "*"
-huggingface-hub = ">=0.16.4,<1.0"
+huggingface-hub = ">=0.19.3,<1.0"
 numpy = ">=1.17"
 packaging = ">=20.0"
 pyyaml = ">=5.1"
 regex = "!=2019.12.17"
 requests = "*"
-safetensors = ">=0.3.1"
-tokenizers = ">=0.14,<0.15"
+safetensors = ">=0.4.1"
+tokenizers = ">=0.14,<0.19"
 tqdm = ">=4.27"
 
 [package.extras]
-accelerate = ["accelerate (>=0.20.3)"]
-agents = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch (>=1.10,!=1.12.0)"]
-all = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.15)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision"]
+accelerate = ["accelerate (>=0.21.0)"]
+agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch (>=1.11,!=1.12.0)"]
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 audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 codecarbon = ["codecarbon (==1.2.0)"]
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 docs-specific = ["hf-doc-builder"]
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 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 ftfy = ["ftfy"]
-integrations = ["optuna", "ray[tune]", "sigopt"]
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 ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
 modelcreation = ["cookiecutter (==1.7.3)"]
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 onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
 onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
 optuna = ["optuna"]
-quality = ["GitPython (<3.1.19)", "black (>=23.1,<24.0)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (>=0.0.241,<=0.0.259)", "urllib3 (<2.0.0)"]
-ray = ["ray[tune]"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
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 retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
 sagemaker = ["sagemaker (>=2.31.0)"]
 sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
@@ -1377,38 +2808,44 @@ serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "timeout-decorator"]
-tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
-tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic (<2)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
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+tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 timm = ["timm"]
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-torch = ["accelerate (>=0.20.3)", "torch (>=1.10,!=1.12.0)"]
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 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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 video = ["av (==9.2.0)", "decord (==0.6.0)"]
-vision = ["Pillow (<10.0.0)"]
+vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
 name = "typing-extensions"
-version = "4.8.0"
+version = "4.9.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
-category = "main"
 optional = false
 python-versions = ">=3.8"
+files = [
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+]
 
 [[package]]
 name = "urllib3"
-version = "2.0.6"
+version = "2.2.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
-category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
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-secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"]
+h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
@@ -1416,9 +2853,12 @@ zstd = ["zstandard (>=0.18.0)"]
 name = "uvicorn"
 version = "0.20.0"
 description = "The lightning-fast ASGI server."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
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 click = ">=7.0"
@@ -1429,16 +2869,19 @@ standard = ["colorama (>=0.4)", "httptools (>=0.5.0)", "python-dotenv (>=0.13)",
 
 [[package]]
 name = "virtualenv"
-version = "20.24.5"
+version = "20.25.0"
 description = "Virtual Python Environment builder"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
+files = [
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 filelock = ">=3.12.2,<4"
-platformdirs = ">=3.9.1,<4"
+platformdirs = ">=3.9.1,<5"
 
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@@ -1446,22 +2889,55 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
 
 [[package]]
 name = "watchdog"
-version = "3.0.0"
+version = "4.0.0"
 description = "Filesystem events monitoring"
-category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
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+]
 
 [package.extras]
 watchmedo = ["PyYAML (>=3.10)"]
 
 [[package]]
 name = "werkzeug"
-version = "3.0.0"
+version = "3.0.1"
 description = "The comprehensive WSGI web application library."
-category = "main"
 optional = false
 python-versions = ">=3.8"
+files = [
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+]
 
 [package.dependencies]
 MarkupSafe = ">=2.1.1"
@@ -1471,11 +2947,14 @@ watchdog = ["watchdog (>=2.3)"]
 
 [[package]]
 name = "wheel"
-version = "0.41.2"
+version = "0.42.0"
 description = "A built-package format for Python"
-category = "main"
 optional = false
 python-versions = ">=3.7"
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+]
 
 [package.extras]
 test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
@@ -1484,17 +2963,99 @@ test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
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-category = "main"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
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-zipp = [
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-    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
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diff --git a/pxtextmining/factories/factory_predict_unlabelled_text.py b/pxtextmining/factories/factory_predict_unlabelled_text.py
index f4a12b7f..673eef65 100644
--- a/pxtextmining/factories/factory_predict_unlabelled_text.py
+++ b/pxtextmining/factories/factory_predict_unlabelled_text.py
@@ -50,7 +50,8 @@ def predict_multilabel_sklearn(
         labels (list, optional): List containing target labels. Defaults to major_cats.
         additional_features (bool, optional): Whether or not FFT_q_standardised is included in data. Defaults to False.
         label_fix (bool, optional): Whether or not the class with the highest probability is taken as the predicted class in cases where no classes are predicted. Defaults to True.
-        rules_dict
+        rules_dict (dict, optional): If supplied, enhances predicted probabilities of specific classes with rules based on the text vocabulary.
+        custom_threshold_dict (dict, optional): If supplied, binary predictions are based on custom thresholds for specific classes. If not specified, defaults to threshold of 0.5.
 
     Returns:
         (pd.DataFrame): DataFrame containing one hot encoded predictions, and a column with a list of the predicted labels.
@@ -116,7 +117,8 @@ def predict_multilabel_bert(
         additional_features (bool, optional): Whether or not FFT_q_standardised is included in data. Defaults to False.
         label_fix (bool, optional): Whether or not the class with the highest probability is taken as the predicted class in cases where no classes are predicted. Defaults to True.
         custom_threshold_dict (dict, optional): If custom thresholds for each label probability should be used. If none provided, default of 0.5 is used where a label is given if the probability is > 0.5. Keys of dict should correspond to labels.
-        rules_dict
+        rules_dict (dict, optional): If supplied, enhances predicted probabilities of specific classes with rules based on the text vocabulary.
+
 
     Returns:
         (pd.DataFrame): DataFrame containing one hot encoded predictions, and a column with a list of the predicted labels.
diff --git a/pxtextmining/factories/factory_write_results.py b/pxtextmining/factories/factory_write_results.py
index bba0de2b..90273652 100644
--- a/pxtextmining/factories/factory_write_results.py
+++ b/pxtextmining/factories/factory_write_results.py
@@ -43,9 +43,16 @@ def write_multilabel_models_and_metrics(models, model_metrics, path):
 def write_model_preds(x, y_true, preds_df, labels, path="labels.xlsx", return_df=False):
     """Writes an Excel file to enable easier analysis of model outputs using the test set. Columns of the Excel file are: comment_id, actual_labels, predicted_labels, actual_label_probs, and predicted_label_probs.
 
-    Currently only works with sklearn models.
-
     Args:
+        x (pd.Series OR pd.DataFrame): Text data used for predictions
+        y_true (np.array): Onehot encoded targets
+        preds_df (pd.DataFrame): DataFrame containing predictions, predicted probabilities, and labels. Should be produced by predict_multilabel_sklearn or predict_multilabel_bert
+        labels (list): List containing target labels.
+        path (str, optional): Filename and path for file to be saved. Defaults to "labels.xlsx".
+        return_df (bool, optional): Whether or not the processed data should be returned as a DataFrame. Defaults to False.
+
+    Returns:
+        (pd.DataFrame): DataFrame containing comment_id, comment text, actual_labels, predicted_labels, actual_label_probs, and predicted_label_probs.
     """
     assert len(x) == len(y_true) == len(preds_df)
     actual_labels = pd.DataFrame(y_true, columns=labels).apply(
diff --git a/pxtextmining/pipelines/multilabel_pipeline.py b/pxtextmining/pipelines/multilabel_pipeline.py
index 6544d4b6..48689025 100644
--- a/pxtextmining/pipelines/multilabel_pipeline.py
+++ b/pxtextmining/pipelines/multilabel_pipeline.py
@@ -52,6 +52,8 @@ def run_sklearn_pipeline(
         target (list, optional): The target labels, which should be columns in the dataset DataFrame. Defaults to major_cats.
         models_to_try (list, optional): List of the estimators to try. Defaults to ["mnb", "knn", "svm", "rfc"]. Permitted values are "mnb" (Multinomial Naive Bayes), "knn" (K Nearest Neighbours), "svm" (Support Vector Classifier), or "rfc" (Random Forest Classifier).
         path (str, optional): Path where the models are to be saved. If path does not exist, it will be created. Defaults to 'test_multilabel'.
+        include_analysis (bool, optional): Whether or not additional Excel files showing model performance and predicted labels are generated. Defaults to False.
+        custom_threshold (bool, optional): Whether or not a custom classification threshold maximising the F1 score is to be calculated. Defaults to False.
     """
     # random_state = random.randint(1, 999)
     if target == major_cats:
@@ -154,6 +156,7 @@ def run_svc_pipeline(
         target (list, optional): The target labels, which should be columns in the dataset DataFrame. Defaults to major_cats.
         path (str, optional): Path where the models are to be saved. If path does not exist, it will be created. Defaults to 'test_multilabel'.
         include_analysis (bool, optional): Whether or not to create Excel files including further analysis of the model's performance. Defaults to False. If True, writes two Excel files to the specified folder, one containing the labels and the performance metrics for each label, and one containing the predicted labels and the actual labels for the test set, with the model's probabilities for both.
+        custom_threshold (bool, optional): Whether or not a custom classification threshold maximising the F1 score is to be calculated. Defaults to False.
 
     """
     # random_state = random.randint(1, 999)
@@ -240,9 +243,10 @@ def run_bert_pipeline(
     Args:
         additional_features (bool, optional): Whether or not additional features (question type and text length) are used. Defaults to False.
         path (str, optional): Path where the models are to be saved. If path does not exist, it will be created. Defaults to 'test_multilabel'.
-        target
-        include_analysis
-        custom_threshold
+        target (list, optional): The target labels, which should be columns in the dataset DataFrame. Defaults to major_cats.
+        include_analysis (bool, optional): Whether or not to create Excel files including further analysis of the model's performance. Defaults to False. If True, writes two Excel files to the specified folder, one containing the labels and the performance metrics for each label, and one containing the predicted labels and the actual labels for the test set, with the model's probabilities for both.
+        custom_threshold (bool, optional): Whether or not a custom classification threshold maximising the F1 score is to be calculated. Defaults to False.
+
     """
     # random_state = random.randint(1, 999)
     print(f"random_state is: {random_state}")
diff --git a/pyproject.toml b/pyproject.toml
index 914bed19..5d3f64c4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,16 +1,16 @@
 [tool.poetry]
 name = "pxtextmining"
-version = "1.0.0"
+version = "1.0.1"
 description = "Text classification of patient experience feedback."
 authors = ['CDU Data Science <phudatascience@nottshc.nhs.uk>',
-'YiWen Hon <yiwen.hon@nottshc.nhs.uk>']
+'YiWen Hon <yiwen.hon1@nhs.net>']
 readme = "README.md"
 license = "MIT"
-repository = "https://github.com/CDU-data-science-team/pxtextmining"
-documentation = "https://cdu-data-science-team.github.io/pxtextmining"
+repository = "https://github.com/the-strategy-unit/pxtextmining"
+documentation = "https://the-strategy-unit.github.io/pxtextmining"
 
 [tool.poetry.dependencies]
-python = ">=3.8, <3.11"
+python = ">3.8, <3.11"
 joblib = "^1.2.0"
 matplotlib = "^3.3.2"
 numpy = ">=1.22"
@@ -45,6 +45,10 @@ mkdocs = "^1.4.2"
 mkdocstrings-python = "^0.8.2"
 mkdocstrings = "^0.19.1"
 
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+pythonpath = ["api"]
+
 [tool.ruff]
 select = ["E", "F", "B"]
 ignore = ["E501"]