Instructions to use macmacmacmac/CLEF-Screen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use macmacmacmac/CLEF-Screen with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
CLEF-Screen: JointSchemaHead redo FT (seed1, 3 epochs) from jev-retrain MI355X run 2026-11-04
2b605e8 verified |
Download README.md from macmacmacmac/CLEF-Screen: direct link, hf CLI and curl.
- Browser
- Download file 1.47 kB
-
https://huggingface.co/macmacmacmac/CLEF-Screen/resolve/main/README.md
- Command line
-
hf download hf://macmacmacmac/CLEF-Screen/README.md
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curl -L -o README.md https://huggingface.co/macmacmacmac/CLEF-Screen/resolve/main/README.md
1.47 kB
| language: | |
| - en | |
| license: other | |
| tags: | |
| - security | |
| - prompt-injection | |
| - litert | |
| # CLEF-Screen | |
| Redo fine-tune of the CLEF-Flash `JointSchemaHead` (121,762,820 parameters, | |
| 122 tensors) on frozen Cloudflare CLEF-Flash LiteRT backbone hidden states. | |
| Only the head is trained; the backbone is unmodified. This repository root | |
| is a complete CLEF LiteRT checkpoint directory with the fine-tuned head. | |
| ## Training | |
| - Compute: DigitalOcean GPU droplet `jev-retrain` (AMD Instinct MI355X, ROCm), encode 2026-11-04 11:31Z, head training finished 18:24Z. | |
| - Method: `ft_clef_bench.py`, 3 epoch(s), lr 3e-05, batch 16, | |
| pos_weight 3.0. Training details and hashes: `redo-provenance.json`; | |
| full log: `redo-training-log.jsonl` when present. | |
| - Head dtype in this repo: BF16 (deployment contract). Trained head was already BF16; no conversion. | |
| ## Evaluation status | |
| Dev (21,977 rows) epoch0 AUC 0.7516 -> epoch3 AUC 0.9899, TPR@0.5 0.9911 FPR@0.5 0.2183, TPR@0.99 0.9583 FPR@0.99 0.0507. Script-run test banking+slack (21,965 rows) AUC 0.9897, TPR@0.99 0.9574 FPR@0.99 0.0537. Caveats: dev/test are 92% positive (~1,695 negatives); FPR@0.5 remains 0.22; test was evaluated by the training script at finish; no independent frozen replication yet. Full numbers: redo-final-results.json. | |
| No frozen-test, adaptive-attack, or independent-replication result exists yet | |
| for this checkpoint unless stated above. Treat all numbers as development | |
| measurements, not deployment guarantees. | |