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salahh297
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doha-checkpoints

Text Classification
Arabic
arabic
ensemble
doha-ai-challenge
Model card Files
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  1. .gitattributes +35 -0
  2. README.md +81 -0
  3. arabertL/fold0.pt +3 -0
  4. arabertL/fold1.pt +3 -0
  5. arabertL/fold2.pt +3 -0
  6. arabertL/fold3.pt +3 -0
  7. arabertL/fold4.pt +3 -0
  8. arb_e8_s2/fold0.pt +3 -0
  9. arb_e8_s2/fold1.pt +3 -0
  10. arb_e8_s2/fold2.pt +3 -0
  11. arb_e8_s2/fold3.pt +3 -0
  12. arb_e8_s2/fold4.pt +3 -0
  13. arbertv2/fold0.pt +3 -0
  14. arbertv2/fold1.pt +3 -0
  15. arbertv2/fold2.pt +3 -0
  16. arbertv2/fold3.pt +3 -0
  17. arbertv2/fold4.pt +3 -0
  18. arbertv2_ht_e10/fold0.pt +3 -0
  19. arbertv2_ht_e10/fold1.pt +3 -0
  20. arbertv2_ht_e10/fold2.pt +3 -0
  21. arbertv2_ht_e10/fold3.pt +3 -0
  22. arbertv2_ht_e10/fold4.pt +3 -0
  23. arbertv2_ht_e12/fold0.pt +3 -0
  24. arbertv2_ht_e12/fold1.pt +3 -0
  25. arbertv2_ht_e12/fold2.pt +3 -0
  26. arbertv2_ht_e12/fold3.pt +3 -0
  27. arbertv2_ht_e12/fold4.pt +3 -0
  28. arbertv2_ht_e8/fold0.pt +3 -0
  29. arbertv2_ht_e8/fold1.pt +3 -0
  30. arbertv2_ht_e8/fold2.pt +3 -0
  31. arbertv2_ht_e8/fold3.pt +3 -0
  32. arbertv2_ht_e8/fold4.pt +3 -0
  33. arbertv2_ht_e8_s3/fold0.pt +3 -0
  34. arbertv2_ht_e8_s3/fold1.pt +3 -0
  35. arbertv2_ht_e8_s3/fold2.pt +3 -0
  36. arbertv2_ht_e8_s3/fold3.pt +3 -0
  37. arbertv2_ht_e8_s3/fold4.pt +3 -0
  38. arbertv2_ht_e8_s4/fold0.pt +3 -0
  39. arbertv2_ht_e8_s4/fold1.pt +3 -0
  40. arbertv2_ht_e8_s4/fold2.pt +3 -0
  41. arbertv2_ht_e8_s4/fold3.pt +3 -0
  42. arbertv2_ht_e8_s4/fold4.pt +3 -0
  43. base_arabertv02_tapt/fold0.pt +3 -0
  44. base_arabertv02_tapt/fold1.pt +3 -0
  45. base_arabertv02_tapt/fold2.pt +3 -0
  46. base_arabertv02_tapt/fold3.pt +3 -0
  47. base_arabertv02_tapt/fold4.pt +3 -0
  48. base_e8_s2/fold0.pt +3 -0
  49. base_e8_s2/fold1.pt +3 -0
  50. base_e8_s2/fold2.pt +3 -0
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ckpt filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ language:
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+ - ar
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+ tags:
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+ - text-classification
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+ - arabic
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+ - ensemble
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+ - doha-ai-challenge
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+ ---
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+
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+ # Doha AI Challenge — fine-tuned checkpoints (Team Salahh)
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+
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+ This repository holds the **fine-tuned model checkpoints** for our winning solution to the
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+ **Google Cloud Doha AI Challenge** (6-target Arabic-news classification, weighted-F1).
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+
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+ The solution is an ensemble of Arabic transformer classifiers built on **9 pretrained backbones**,
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+ each fine-tuned under several configurations/seeds. Every run is trained with 5-fold cross-validation,
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+ so each of the 42 runs contributes **5 fold checkpoints**:
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+
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+ ```
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+ <tag>/fold0.pt fold1.pt fold2.pt fold3.pt fold4.pt # 42 tags x 5 folds = 210 checkpoints
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+ ```
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+
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+ Each `fold*.pt` is a `state_dict` (bf16) of a multi-task model: a shared encoder + 6 linear
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+ classification heads (one per target). ~92 GB total.
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+
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+ These checkpoints are consumed by the **`solution/`** code of the competition submission package
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+ (`infer.py` / `reproduce_from_checkpoints.ipynb`), together with `recipe.json` (per-target model sets)
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+ and `weights.json` (ensemble weights), to reproduce the final `clean.csv`.
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+
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+ ---
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+
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+ ## 1. Download the checkpoints
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+
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+ ```bash
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+ pip install -U "huggingface_hub[cli]"
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+
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+ # into the solution/ folder of the submission package, as checkpoints/<tag>/fold*.pt
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+ huggingface-cli download salahh297/doha-checkpoints \
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+ --repo-type model --local-dir solution/checkpoints
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+ ```
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+ (Or in Python:)
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+ ```python
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+ from huggingface_hub import snapshot_download
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+ snapshot_download("salahh297/doha-checkpoints", repo_type="model",
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+ local_dir="solution/checkpoints")
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+ ```
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+
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+ After download, the layout is `solution/checkpoints/<tag>/fold{0..4}.pt`.
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+
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+ ---
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+
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+ ## 2. Reproduce `clean.csv` from the checkpoints (GPU)
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+
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+ You need the competition **`solution/`** folder (code + `recipe.json` + `weights.json` +
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+ `data/Train.csv`, `data/Test.csv`) alongside the downloaded `checkpoints/`.
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+
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+ ```bash
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+ cd solution
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+ pip install torch "transformers==4.57.1" accelerate sentencepiece arabert farasapy scikit-learn pandas numpy
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+
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+ # script:
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+ python3 infer.py
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+ # or the notebook (same result):
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+ # jupyter nbconvert --to notebook --execute reproduce_from_checkpoints.ipynb
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+ ```
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+
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+ `infer.py` rebuilds each model, loads its five fold checkpoints from `checkpoints/<tag>/fold*.pt`,
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+ runs them forward on the test set (GPU), averages the five test-probability sets per model, and
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+ combines the per-target model sets (`recipe.json`) with the ensemble weights (`weights.json`) to
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+ write `clean.csv`.
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+
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+ - **GPU:** A100 40GB, or A100 / H100 80GB (H100 80GB recommended).
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+ - The farasa-segmented run preprocesses the test text with the farasa segmenter and is slow
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+ (tens of minutes); all other runs are fast.
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+ - Fixed seeds are used throughout, so the pipeline is reproducible.
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+
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+ ---
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+
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+ _Team: Salahh._
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