mhhh, I guess, I’ve been to positiv too early 
The problem I am having now is that I can train a model and it realy seems to generate positive accuracy/loss results but the prediction always generates the same class 
The CoLab notebook is here using the 20 newsgroups dataset as a sample.
Am I using the wrong way to train or is the prediction not correct?

  
      github.com
  
  
    Dirkster99/PyNotes/blob/master/Transformers/LocalModelUsage_Finetuning/62_Transformer_4_Language_Classification_MultiClass.ipynb 29
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "62 Transformer 4 Language Classification MultiClass.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOyEXhZWk3x+tCwEb02x5Az",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "c60ae33f833a4093a9323b17a9a6f379": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",


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I’ve tried this with different models and different datasets but the result always seems to be the same - the model just always predicts 1 class no matter what I try to enter as sample text