
# 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
{"language": "en", "license": "apache-2.0", "tags": ["distilbert", "needmining"], "metric": ["f1"]}
transformers pytorch distilbert text-classification needmining en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #needmining #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

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:


### 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:



### BibTeX entry and citation info


coming soon



### How to use


You can use this model directly with a pipeline for text classification: ### 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: ### BibTeX entry and citation info


coming soon
TAGS
#transformers #pytorch #distilbert #text-classification #needmining #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### How to use


You can use this model directly with a pipeline for text classification: ### 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: ### BibTeX entry and citation info


coming soon
passage: TAGS
#transformers #pytorch #distilbert #text-classification #needmining #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### How to use


You can use this model directly with a pipeline for text classification:### 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:### BibTeX entry and citation info


coming soon