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README.md

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+---
+library_name: transformers
+pipeline_tag: summarization
+---
+# Populism Detection & Summarization
+
+This checkpoint is a BART-based, LoRA-fine-tuned model that does two things:
+
+Summarizes party press releases (and, when relevant, explains where populist framing appears), and
+
+Classifies whether the text contains populist language (Is_Populist ∈ {0,1}).
+
+Weights here are the merged LoRA result—no adapters required.
+
+The model was trained on ~10k official party press releases from 12 countries (Italy, Sweden, Switzerland, Netherlands, Germany, Denmark, Spain, UK, Austria, Poland, Ireland, France) that were labeled and summarized via a Palantir AIP Ontology step using GPT-4o.
+
+## Model Details
+
+Pretrained Model: facebook/bart-base (seq2seq) fine-tuned with LoRA and then merged.
+Instruction Framing: Two prefixes:
+
+Summarize: summarize: <original_text>
+
+Classify: classify_populism: <original_text> → model outputs 0 or 1 (or you can argmax over first decoder step logits for tokens “0” vs “1”).
+
+Tokenization: BART’s subword tokenizer (Byte-Pair Encoding).
+
+Input Processing: Text is truncated to 1024 tokens; summaries capped at 128 tokens.
+
+Output Generation (summarization): beam search (typically 5 beams), mild length penalty, and no-repeat bigrams to reduce redundancy.
+
+Key Parameters:
+
+Max Input Length: 1024 tokens — fits long releases while controlling memory.
+
+Max Target Length: 128 tokens — concise summaries with good coverage.
+
+Beam Search: ~5 beams — balances quality and speed.
+
+Classification Decoding: read the first generated token (0/1) or take first-step logits for a deterministic argmax.
+
+Generation Process (high level)
+
+Input Tokenization: Convert text to subwords and build the encoder input.
+
+Beam Search (summarize): Explore multiple candidate sequences, pick the most probable.
+
+Output Decoding: Map token IDs back to text, skipping special tokens.
+
+Model Hub: tdickson17/Populism_detection
+
+Repository: https://github.com/tcdickson/Populism.git
+
+## Training Details
+
+Data Collection:
+Press releases were scraped from official party websites to capture formal statements and policy messaging. A Palantir AIP Ontology step (powered by GPT-4o) produced:
+
+Is_Populist (binary) — whether the text exhibits populist framing (e.g., “people vs. elites,” anti-institutional rhetoric).
+
+Summaries/Explanations — concise abstracts; when populism is present, the text explains where/how it appears.
+
+Preprocessing:
+HTML/boilerplate removal, normalization, and formatting into pairs:
+
+Input: original release text (title optional at inference)
+
+Targets: (a) abstract summary/explanation, (b) binary label
+
+Training Objective:
+Supervised fine-tuning for joint tasks:
+
+Abstractive summarization (seq2seq cross-entropy)
+
+Binary classification (decoded 0/1 via the same seq2seq head)
+
+Training Strategy:
+
+Base: facebook/bart-base
+
+Method: LoRA on attention/FFN blocks (r=16, α=32, dropout=0.05), then merged into base.
+
+Decoding: beam search for summaries; argmax or short generation for labels.
+
+Evaluation signals: ROUGE for summaries; Accuracy/Precision/Recall/F1 for classification.
+
+This setup lets one checkpoint handle both analysis (populism flag) and explanation (summary) with simple instruction prefixes.
+
+## Citation:
+
+@article{dickson2024going,
+  title={Going against the grain: Climate change as a wedge issue for the radical right},
+  author={Dickson, Zachary P and Hobolt, Sara B},
+  journal={Comparative Political Studies},
+  year={2024},
+  publisher={SAGE Publications Sage CA: Los Angeles, CA}
+}