Update README.md

README.md

@@ -5,55 +5,11 @@ tags: []
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
+Fine-tuned [XLM-R Large](https://huggingface.co/FacebookAI/xlm-roberta-large) for task of classifying sentences as polarizing or not. The taxonomy for polarizing claims follows Ashraf et al. 2024. The model was first trained on a Telegram dataset that was annotated using GPT-4o with this [prompt](https://huggingface.co/Sami92/XLM-R-Large-Polarization-Classifier/blob/main/PolarizationPrompt_GPT.txt). In a second step it was trained on the data from Ashraf et al. 2024.
 
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
@@ -61,17 +17,22 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 [More Information Needed]
 
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from transformers import pipeline
 
-[More Information Needed]
+texts = [
+       'Afghanistan - Warum die Taliban Frauenrechte immer mehr einschränken\nhttps://t.co/rhwOdNoJUx',
+       '#Münster #G7 oder "Ab jetzt außen rumfahren". https://t.co/Goj5vtrnst',
+       'Interessantes Trio.\nDie eine hat eine Wahl vergeigt, die andere kungelt mit Putin und die Dritte hat die Hilfe nach der Flutkatastrophe nicht auf die Reihe bekommen. \nMehr Frauen an die Macht!',
+       'Wie kann man sich #AnneWill betrachten ohne das übertragende Gerät zu zerschmettern. Eben 20 sec. dem #FDP Watschengesicht beim Quaken zugehört. Du lieber Himmel, wie weltfremd geht´s denn noch.'
+  ]
+checkpoint = "Sami92/XLM-R-Large-Polarization-Classifier"
+tokenizer_kwargs = {'padding':True,'truncation':True,'max_length':512}
+polarization_classifier = pipeline("text-classification", model = checkpoint, tokenizer =checkpoint, **tokenizer_kwargs, device="cuda")
+polarization_classifier(texts)
 
 ## Training Details
 
@@ -92,13 +53,21 @@ Use the code below to get started with the model.
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+Weakly-supervised Training on Telegram Data
 
-#### Speeds, Sizes, Times [optional]
+- Epochs: 10
+- Batch size: 16
+- learning_rate: 2e-5
+- weight_decay: 0.01
+- fp16: True
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+Supervised Training on Ashraf et al. 2024
 
-[More Information Needed]
+- Epochs: 10
+- Batch size: 16
+- learning_rate: 2e-5
+- weight_decay: 0.01
+- fp16: True
 
 ## Evaluation
 
@@ -112,88 +81,31 @@ Use the code below to get started with the model.
 
 [More Information Needed]
 
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
 
 ### Results
+| Category            | Precision | Recall | F1-Score | Support |
+|---------------------|:---------:|:------:|:--------:|:-------:|
+| **non-polarization** |   0.89    |  0.89  |   0.89   |  1350   |
+| **polarization**     |   0.67    |  0.67  |   0.67   |   463   |
+|                     |           |        |          |         |
+| **Accuracy**         |           |        |   0.83   |  1813   |
+| **Macro avg**        |   0.78    |  0.78  |   0.78   |  1813   |
+| **Weighted avg**     |   0.83    |  0.83  |   0.83   |  1813   |
 
-[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).
-
-- **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
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```bibtex
+
+@inproceedings{ashraf_defakts_2024,
+	address = {Torino, Italia},
+	title = {{DeFaktS}: {A} {German} {Dataset} for {Fine}-{Grained} {Disinformation} {Detection} through {Social} {Media} {Framing}},
+	shorttitle = {{DeFaktS}},
+	url = {https://aclanthology.org/2024.lrec-main.409},
+	booktitle = {Proceedings of the 2024 {Joint} {International} {Conference} on {Computational} {Linguistics}, {Language} {Resources} and {Evaluation} ({LREC}-{COLING} 2024)},
+	publisher = {ELRA and ICCL},
+	author = {Ashraf, Shaina and Bezzaoui, Isabel and Andone, Ionut and Markowetz, Alexander and Fegert, Jonas and Flek, Lucie},
+	editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},
+	year = {2024},
+}
+```