Hi Philip,
maybe the HF Pipelines implementation could help:

  
      github.com
  
  
    huggingface/transformers/blob/master/notebooks/03-pipelines.ipynb 2
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"


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It should also support e.g. multiple sentences (List[str]) as inputs and you could use of course own trained and fine-tuned models. Detailed implementation can be found here:

  
      github.com
  
  
    huggingface/transformers/blob/master/src/transformers/pipelines.py 3
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import csv
import json
import os
import pickle


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To use it for text classification/sentiment analysis:

nlp_sentence_classif = pipeline('sentiment-analysis')
nlp_sentence_classif('Such a nice weather outside !')

and it also outputs the score:
[{'label': 'POSITIVE', 'score': 0.9997656}]

I like using Pipelines especially for testing NER models