readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert-base-uncased-finetuned-greenpatent
    results: []
  widget:
  - text: A method for recycling waste
  - text: A method of reducing pollution
  - text: An apparatus to improve environmental aspects
  - text: A method to improve waste management
  - text: A device to use renewable energy sources
  datasets:
  - cwinkler/green_patents
  language:
  - en
  pipeline_tag: text-classification
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # Classification of patent title - "green" or "no green"
  
  This model classifies patents into "green patents" or "no green patents" by their titles.
  
  ### Examples of "green patents" titles:
  
  - "A method for recycling waste" - score: 0.714 
  - "A method of reducing pollution" - score: 0.786
  - "An apparatus to improve environmental aspects" - score: 0.570
  - "A method to improve waste management" - score: 0.813
  - "A device to use renewable energy sources" - score: 0.98
  - "A technology for efficient electrical power generation"- score: 0.975
  - "A method for the production of fuel of non-fossil origin" - score: 0.975
  - "Biofuels from waste" - score: 0.88
  - "A combustion technology with mitigation potential" - score: 0.947
  - "A device to capture greenhouse gases" - score: 0.871
  - "A method to reduce the greenhouse effect" - score: 0.887
  - "A device to improve the climate" - score: 0.650
  - "A device to stop climate change" - score: 0.55
  
  
  ### Examples of "no green patents" titles:
  
  - "A device to destroy the nature" - score: 0.19
  - "A method to produce smoke" - score: 0.386
  
  ### Examples of the model's limitation
  
  - "A method to avoid trash" - score: 0.165
  - "A method to reduce trash" - score: 0.333
  - "A method to burn the Amazonas" - score: 0.501
  - "A method to burn wood" - score: 0.408
  - "Green plastics" - score: 0.126
  - "Greta Thunberg" - score: 0.313 (How dare you, model?); BUT: "A method of using Greta Thunberg to stop climate change" - score: 0.715
  
  Examples were inspired by https://www.epo.org/news-events/in-focus/classification/classification.html
  
  # distilbert-base-uncased-finetuned-greenpatent
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [green patent dataset](https://huggingface.co/datasets/cwinkler/green_patents). The green patent dataset was split into 70 % training data and 30 % test data (using ".train_test_split(test_size=0.3)").
  The model achieves the following results on the evaluation set:
  - Loss: 0.3148
  - Accuracy: 0.8776
  - F1: 0.8770
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 2
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.4342        | 1.0   | 101  | 0.3256          | 0.8721   | 0.8712 |
  | 0.3229        | 2.0   | 202  | 0.3148          | 0.8776   | 0.8770 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cpu
  - Datasets 2.8.0
  - Tokenizers 0.13.2