README.md

---
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