Update README.md

Add carbon emissions

README.md

@@ -12,7 +12,22 @@ pipeline_tag: text-classification
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
+This is a deBERTa-v3-base model with an adapter trained on X tweets from [More Information Needed] finetuned for text classification. The model predicts whether a tweet supports a given conspiracy theory or not. The model was trained on tweets related to six common COVID-19 conspiracy theories.
 
+1. **Vaccines are unsafe** The coronavirus vaccine is either unsafe or part of a larger plot to control people or reduce the population. 
+
+2. **Governments and politicians spread misinformation** Politicians or government agencies are intentionally spreading false information, or they have some other motive for the way they are responding to the coronavirus. 
+
+3. **The Chinese intentionally spread the virus** The Chinese government intentionally created or spread the coronavirus to harm other countries. 
+
+4. **Deliberate strategy to create economic instability or benefit large corporations** The coronavirus or the government's response to it is a deliberate strategy to create economic instability or to benefit large corporations over small businesses. 
+
+5. **Public intentionally misled about the true nature of the virus and prevention** The public is being intentionally misled about the true nature of the Coronavirus, its risks, or the efficacy of certain treatments or prevention methods. 
+
+6. **Human made and bioweapon** The Coronavirus was created intentionally, made by humans, or as a bioweapon. 
+
+
+This model is suitable for English.
 
 - **Developed by:** Webimmunication Team
 - **Shared by [optional]:** @ikrysinska
@@ -79,7 +94,7 @@ Use the code below to get started with the model.
 
 #### Preprocessing [optional]
 
-[More Information Needed]
+- hashtags, mentions, punctuation.
 
 
 #### Training Hyperparameters
@@ -94,7 +109,7 @@ Use the code below to get started with the model.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+The model was evaluated on a sample 
 
 ### Testing Data, Factors & Metrics
 
@@ -136,29 +151,12 @@ Use the code below to get started with the model.
 
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
+- **Hardware Type:** GPU Tesla V100
+- **Hours used:** 40
+- **Cloud Provider:** Google Cloud Platform
+- **Compute Region:** us-east1
+- **Carbon Emitted:** 4.44 kg CO2 ([equivalent to: 17.9 km driven by an average ICE car, 2.22 kgs of coal burned, 0.07 tree seedlings sequesting carbon for 10 years](https://www.epa.gov/energy/greenhouse-gases-equivalencies-calculator-calculations-and-references)
 
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
 
 ## Citation [optional]