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. 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:
>>> 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) and optimized for f1 score on a validation set of 1600 sentences.
Preprocessing
The preprocessing follows the Distilbert base model.
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