Update README.md

README.md

@@ -12,4 +12,61 @@ tags:
 - social science
 - communication
 - hatespeech
----
+---
+This model card provides details about the DistilBERT-based classifier designed to detect fear speech (FS) in German language Telegram posts. The classifier was developed to study the prevalence and dynamics of FS in the communication of radical and extremist actors on Telegram.
+Model Details
+Model Description
+
+The distilbert_fearspeech_classifier is a fine-tuned DistilBERT model aimed at identifying and classifying fear speech in social media posts, particularly those from radical and extremist groups. It has been trained on a dataset of annotated Telegram posts from far-right, COVID-19 protest, and conspiracy-focused actors.
+
+    Developed by: Simon Greipl, Julian Hohner, Heidi Schulze, Patrick Schwabl, Diana Rieger
+    Model type: DistilBERT for text classification
+    Language(s) (NLP): German
+    License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License
+
+Model Sources
+
+    Paper: "You are doomed!" Crisis-specific and Dynamic Use of Fear Speech in Protest and Extremist Radical Social Movements
+
+
+Uses
+  
+  The model is used directly to classify Telegram posts into fear speech (FS) and non-fear speech (no FS) categories. This is particularly useful for researchers studying online radicalization and the dynamics of fear speech in social media.
+
+Downstream Use
+  
+  The model can be fine-tuned for specific tasks related to hate speech detection, communication studies, and social media analysis.
+  Out-of-Scope Use
+  
+  This model should not be used for labeling and penalizing individuals or groups without context or understanding of the nuances in their communication. Misuse could lead to unjust outcomes.
+  Bias, Risks, and Limitations
+  
+  The model was trained on data from specific Telegram channels and groups known for their extremist content. Inherent biases in the data may affect the model's predictions. Misuse of the model could lead to labeling and penalizing individuals or groups without proper context.
+  Recommendations
+  
+  Users should be aware of the risks, biases, and limitations of the model. Further research and contextual understanding are recommended before using the model for critical decision-making.
+  How to Get Started with the Model
+
+Use the following code to get started with the model:
+
+```
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("PatrickSchwabl/distilbert_fearspeech_classifier")
+model = AutoModelForSequenceClassification.from_pretrained("PatrickSchwabl/distilbert_fearspeech_classifier")
+
+# Tokenize input text
+inputs = tokenizer("Your text here", return_tensors="pt")
+
+# Get model predictions
+outputs = model(**inputs)
+predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+
+# Print predictions
+print(predictions)
+
+```
+
+