Update README.md

README.md

@@ -5,7 +5,7 @@ tags: []
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model is fine-tuned for topic classification and uses the labels provided by the Comparative Agendas project. It can be used for the downstream task of classyfing Telegram Posts into 23 policy areas. It is similar to [partypress/partypress-multilingual](https://huggingface.co/partypress/partypress-multilingual), however, its base model is FacebookAI/xlm-roberta-large and it was fine-tuned on more data and different data sources.
 
 
 
@@ -13,47 +13,9 @@ tags: []
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This model is based on FacebookAI/xlm-roberta-large and was trained in a three-step process. In the first step a dataset of press releases was weakly labeled with GPT-4o and the model was trained on the data. In a second step, it was fine-tuned again with GPT-4o labeled data but this time the dataset was drawn from Telegram. In a third step, it was trained on the same human annotated dataset as partypress/partypress-multilingual. The weak pre-training led to improved results on Telegram data.
 
-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
 
@@ -61,71 +23,60 @@ 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 = ['Neue Anschuldigungen gegen die russischen Angriffstruppen in der Ukraine: Laut den USA sollen diese Chlorpikrin als Kampfstoff verwendet haben. Das sei ein Verstoß gegen die Chemiewaffenkonvention. /',
+            'Tiktok ist ja eine chinesische App. Bestimmt wird bald über eine Tonaufnahme diskutiert, die der tschechische Geheimdienst aufgezeichnet hat: Krah am Telefon mit einem chinesischen Tech-Entwickler im TikTok-Business, der den Algorithmus extra zu Gunsten der AfD manipuliert.',
+            'Saubere Bluttransfusion ,dem normalen Menschen ,ist die eigen Blut Spende nicht mehr erlaubt bzw gibt es wiedermal die Empfehlung von hochrangiger Stelle an Blutspendedienste und Krankenhäuser dieses nicht zu ermöglichen.In gehobenen Kreisen sind private Dienstleister in dieser Nische sehr aktiv.']
 
-## Training Details
+>>> tokenizer_kwargs = {'padding':True,'truncation':True,'max_length':512}
+>>> partypress_telegram = pipeline("text-classification", model = "Sami92/XLM-R-Large-PartyPress-Telegram", tokenizer = "Sami92/XLM-R-Large-PartyPress-Telegram", **tokenizer_kwargs)
 
-### Training Data
+>>> partypress_telegram(texts)
+```
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+## Training Details
 
-[More Information Needed]
+### Training Data
 
-### Training Procedure
+The model was trained on three datasets, each based on the data from partypress/partypress-multilingual. The first dataset was weakly labeled using GPT-4o. The [prompt](https://huggingface.co/Sami92/XLM-R-Large-PartyPress/blob/main/FinalPromptPartyPress.txt) contained the label description taken from [Erfort et al. (2023)](https://journals.sagepub.com/doi/10.1177/20531680231183512). The weakly labeled dataset contains 32,060 press releases.
+The second dataset was drawn from Telegram channels. More specifically a sample from about 200 channels that have been subject to a fact-check from either Correctiv, dpa, Faktenfuchs or AFP. 7741 posts were sampled and weakly annotated by GPT-4o with the same prompt as before.
+The third dataset is the human-annotated dataset that is used for training partypress/partypress-multilingual. For training only the single-coded examples were used (24,117). Evaluation was performed on the data that is annotated by two human coders per example (3,121).
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-#### Preprocessing [optional]
 
-[More Information Needed]
 
 
 #### 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 -->
 
-#### 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. -->
 
-[More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
 
-#### Testing Data
+### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+The testing was performed on the same data as for the [Sami92/XLM-R-Large-PartyPress](https://huggingface.co/Sami92/XLM-R-Large-PartyPress/edit/main/README.md). Due to the extra training step on the Telegram data, the F1-score on press releases reduced from 0.72 to 0.62.
 
-#### Factors
+However, for the second test, there is an improvement. For testing on Telegram data, a sample of 84 posts was taken and labeled by the model. Three annotators were then asked if the prediction of the model is either a main topic of the post, a subtopic, or incorrect. The majority vote was used as final label. The detailed results can be found below. For 93% of the Telegram posts, the model prediction was either a main or subtopic. For [Sami92/XLM-R-Large-PartyPress](https://huggingface.co/Sami92/XLM-R-Large-PartyPress/edit/main/README.md) it was only in 88% of the cases a main or subtopic. The improvement is even more visible when focusing on main topics only. For the Telegram-fine-tuned model the prediction is a main topic in 82% of the cases and for the model without training on Telegram data it is 75%.
 
-<!-- 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
 
+
 | Label      | Percentage | Count |
 |:------------:|:----------:|:-----:|
 | **Maintopic** |    0.82    |  69  |
@@ -171,68 +122,6 @@ Use the code below to get started with the model.
 
 
 
-## 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]
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-### Compute Infrastructure
-
-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
-
-[More Information Needed]
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-## 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:**
-
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-
-**APA:**
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
-
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-## Model Card Contact
+## Acknowledgements
 
-[More Information Needed]
+I thank Cornelius Erfort for making the annotated press releases available.