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Add Dockerfile and requirements for HuggingFace Space deployment
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>b43c40e -
Add HuggingFace Spaces metadata to README
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>b848d5b
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Pin transformers==4.57.6 and add runtime version guard
transformers 5.x silently produces wrong predictions (all 'unknown') when loading a 4.x checkpoint — no error is raised. Pinning the exact version the model was saved with, and adding a startup check, makes the failure loud and clear instead of invisible. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>21d2cbd -
Remove Reddit scraper, add IBM Claim Stance loader
Reddit's Responsible Builder Policy prohibits using scraped data to train ML/AI models and requires RFR Program approval for all research use. - Remove data/loaders/reddit.py and .env.example - Drop praw from requirements.txt - Add data/loaders/claim_stance.py (ibm/claim_stance via HuggingFace): ~2,400 claim/counter-claim pairs with PRO/CON stance labels, auto-downloads - Update data/__init__.py and loaders/__init__.py exports accordingly - Update data/README.md to document the new source Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>e82a201 -
Add failure mode analysis
Duzhengyi732c7a6
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Remove evaluation review markdown
3311798 -
Update app.py
c95b0d0 -
Update Readme
7adb320 -
Update evaluation predictions and figures
193007d -
Clean custom evaluation dataset metadata
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Track evaluation outputs while ignoring local model weights
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Add Streamlit argument role demo
2c594c6 -
Add evaluation scripts and result artifacts
72b0375 -
Add custom argument evaluation dataset
028ed9a -
Update dependencies for evaluation and Streamlit demo
4f5c113 -
Update dependencies for evaluation and Streamlit demo
4f39cf5
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Modifying the gitignore in order to have the weights for Pengchong
stefhooy666ae44 -
Ran 3rd 1 epoch, now ran 3 epochs at once (6 epochs trained now)
stefhooye28aef2
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Ran a 2nd epoch
stefhooyf054892 -
Added CMV loader file, ConvoKit
stefhooyb817617
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First run has been completed
stefhooypython -m src.train --epochs 1 --batch-size 8: Loading data... IBM Debater: 34 debates CMV file missing — loading ConvoKit corpus… CMV (ConvoKit): 2982 debates (total: 3016) Device: cuda 50395 total examples Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. Epoch 1/1: 100%|███████████████████████████████████████████████████████████████| 5355/5355 [56:47<00:00, 1.57it/s] Epoch 1: train_loss=0.6114 val_loss=0.5064 val_acc=0.7742 val_f1=0.8093 → Saved best checkpoint (F1=0.8093) to models\best/ Training complete. Best val F1: 0.8093e1f2faf -
Updating the Data Model : IBM Debater vs CMV, testing out CVM and check if it's good or not
stefhooyhttps://www.cs.cornell.edu/~cristian/pdfs/winning_arguments.pdf https://convokit.cornell.edu/documentation/winning.html94abcb8
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Created src/dataset.py — converts Debate objects into PyTorch tensors for RoBERTa
stefhooyCreated src/model.py — loads roberta-base configured for 4-class classification Created src/train.py — full training loop with class weights, AdamW optimizer, validation, checkpoint saving Created src/__init__.py — predict() and predict_debate() functions for other team members to use Fixed Person 1's IBM loader bug (was ignoring stance_WA, labeling everything as premise) Trained a first model (wrong labels, now discarded) What still needs to be done right now: Retrain with the fixed labels — run python -m src.train --epochs 3 --batch-size 8 Sanity check the model once training finishes — run a quick predict test to confirm it returns different labels for different inputs Commit and push the fixed code to the repo so the rest of the team can use predict() and predict_debate()deefbae -
Setting up the training model first draft and also updating the requirements.txt for the second part of this project.
stefhooy28e0f73
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Add data loading pipeline (Person 1)
Unified schema (Argument, Debate dataclasses), loaders for CMV offline dataset, IBM Debater via HuggingFace, and a live Reddit scraper via PRAW. Preprocessing module strips Reddit noise and filters short/deleted text. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>54cd75a -
Initial project scaffold
Sets up directory structure for 6-person team split: data, src, evaluation, analysis, report, notebooks. Each person owns one top-level directory. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>3e68fce