readme: ---
  language: en
  tags:
  - distilbert
  - needmining
  license: apache-2.0
  metric:
  - f1
  ---
  
  # Finetuned-Distilbert-needmining (uncased)
  
  This model is a finetuned version of the [Distilbert base model](https://huggingface.co/distilbert-base-uncased). It was
  trained to predict need-containing sentences from amazon product reviews.
  
  ## Model description
  
  This mode is part of ongoing research, after the publication of the research more information will be added.
  
  ## Intended uses & limitations
  
  You can use this model to identify sentences that contain customer needs in user-generated content. This can act as a filtering process to remove uninformative content for market research.
  
  ### How to use
  
  You can use this model directly with a pipeline for text classification:
  
  ```python
  >>> from transformers import pipeline
  >>> classifier = pipeline("text-classification", model="svenstahlmann/finetuned-distilbert-needmining")
  >>> classifier("the plasic feels super cheap.")
  [{'label': 'contains need', 'score': 0.9397542476654053}]
  ```
  
  ### Limitations and bias
  
  We are not aware of any bias in the training data.
  
  ## Training data
  
  The training was done on a dataset of 6400 sentences. The sentences were taken from product reviews off amazon and coded if they express customer needs.
  ## Training procedure
  For the training, we used [Population Based Training (PBT)](https://www.deepmind.com/blog/population-based-training-of-neural-networks) and optimized for f1 score on a validation set of 1600 sentences.
  
  
  ### Preprocessing
  
  The preprocessing follows the [Distilbert base model](https://huggingface.co/distilbert-base-uncased).
  
  
  ### Pretraining
  
  The model was trained on a titan RTX for 1 hour.
  
  ## Evaluation results
  
   Results on the validation set:
  
  | F1 | 
  |:----:|
  |   76.0   | 
  
  
  ### BibTeX entry and citation info
  
  coming soon