Create README.md

README.md

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+---
+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