Create README.md

README.md

@@ -0,0 +1,57 @@
+---
+widget:
+- text: "thank you for the help :)"
+  example_title: "Positive example"
+- text: "I will have a look. You can find more info in the documentation."
+  example_title: "Neutral example"
+- text: "I hate this new tool, this is bad."
+  example_title: "Negative example"
+---
+
+
+# Finetuned BERT model for classifying community posts
+
+This distilbert model was fine-tuned on ~20.000 community postings using the HuggingFace adapter from Kern AI refinery. 
+The postings consistet of comments and posts from various forums and social media sites.
+For the finetuning, a single NVidia K80 was used for about two hours. 
+
+Join our Discord if you have questions about this model: https://discord.gg/MdZyqSxKbe
+
+BERT, which stands for Bidirectional Encoder Representations from Transformers, is a language model introduced by Google researchers in 2018. 
+It’s designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers2.
+
+BERT is based on the transformer architecture and uses WordPiece to convert each English word into an integer code. 
+This model has a classification head on top of it, which means that this BERT model is specifically made for text classification. 
+
+## Features
+
+- The model can handle various text classification tasks, especially when it comes to postings made in forums and community sites.
+- The output of the model are the three classes "positive", "neutral" and "negative" plus the models respective confidence score of the class. 
+- The model was fine-tuned on a custom datasets that was curated by Kern AI and labeled in our tool refinery.
+- The model is currently supported by the PyTorch framework and can be easily deployed on various platforms using the HuggingFace Pipeline API.
+
+## Usage
+
+To use the model, you need to install the HuggingFace Transformers library:
+
+```bash
+pip install transformers
+```
+Then you can load the model and the tokenizer from the HuggingFace Hub:
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("KernAI/community-sentiment-bert")
+tokenizer = AutoTokenizer.from_pretrained("KernAI/community-sentiment-bert")
+```
+To classify a single sentence or a sentence pair, you can use the HuggingFace Pipeline API:
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+result = classifier("This is a positive sentence.")
+print(result)
+# [{'label': 'Positive', 'score': 0.9998656511306763}]
+```