Update README.md

Add evaluation metrics

README.md

@@ -12,7 +12,7 @@ pipeline_tag: text-classification
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
-This is a  DeBERTa-v3-base-tasksource-nli model with an adapter trained on [More Information Needed, which contains X pairs of a tweet and a conspiracy theory along with class labels: support, denies, neutral. The model was finetuned for text classification to predict whether a tweet supports a given conspiracy theory or not. The model was trained on tweets related to six common COVID-19 conspiracy theories.
+This is a  DeBERTa-v3-base-tasksource-nli model with an adapter trained on [More Information Needed], which contains X pairs of a tweet and a conspiracy theory along with class labels: support, deny, neutral. The model was finetuned for text classification to predict 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. 
 
@@ -60,7 +60,6 @@ This model is suitable for English only.
 
 ## Bias, Risks, and Limitations
 
-
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
 [More Information Needed]
@@ -89,59 +88,35 @@ Use the code below to get started with the model.
 
 The adapter was trained for 5 epochs with a batch size of 16. 
 
-#### Preprocessing [optional]
+#### Preprocessing
 
 The training data was cleaned before the training. All URLs, Twitter user mentions, and non-ASCII characters were removed. 
 
 ## Evaluation
 
-The model was evaluated on a sample of the tweets collected during the COVID-19 pandemic. All the tweets were rated against each of the six theories by five annotators. Using sliding scales, they rated each tweets' endorsement likelihood for the respective conspiracy theory from 0% to 100%. The consensus among raters was substantial for every conspiracy theory (see table below). 
-
-### Testing Data, Factors & Metrics
+The model was evaluated on a sample of the tweets collected during the COVID-19 pandemic. All the tweets were rated against each of the six theories by five annotators. Using sliding scales, they rated each tweets' endorsement likelihood for the respective conspiracy theory from 0% to 100%. The consensus among raters was substantial for every conspiracy theory. Comparisons with human evaluations revealed substantial correlations. The model significantly surpasses the performance of the pre-trained model without the finetuned adapter (see table below). 
 
-#### Testing Data
 
-<!-- This should link to a Data Card if possible. -->
-
-[More Information Needed]
+| Conspiracy Theory  | Correlations between human raters  | Correlation between human ratings and model without adapter  | Correlation between human ratings and model with finetuned adapter |
+|---|---|---|---|
+| **Vaccines are unsafe.** | 0.78 | 0.29 | 0.57 |
+| **Governments and politicians spread misinformation.**  | 0.58 | 0.32 | 0.72 | 
+| **The Chinese intentionally spread the virus.**  |  0.62  | 0.53  |  0.64  |
+| **Deliberate strategy to create economic instability or benefit large corporations.** | 0.56 | 0.33 | 0.54 |
+| **Public was intentionally misled about the true nature of the virus and prevention.** | 0.66 | 0.37 | 0.68 | 
+| **Human made and bioweapon.** | 0.67 | 0.15 | .78 |
 
-#### Factors
-
-The evaluation dataset
-
-[More Information Needed]
 
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
+Carbon emissions are 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:** 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)
+- **Carbon Emitted:** 4.44 kg CO2 eq ([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)
 
 
 ## Citation [optional]
@@ -162,16 +137,15 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 [More Information Needed]
 
-## More Information [optional]
-
-[More Information Needed]
 
-## Model Card Authors [optional]
+## Model Card Authors
 
 @ikrysinska, @wtomi
 
 ## Model Card Contact
 
 izabela.krysinska@doctorate.put.poznan.pl
+
 tomi.wojtowicz@doctorate.put.poznan.pl
+
 mikolaj.morzy@put.poznan.pl