Instructions to use ModerRAS/AniFileBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModerRAS/AniFileBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ModerRAS/AniFileBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ModerRAS/AniFileBERT") model = AutoModelForTokenClassification.from_pretrained("ModerRAS/AniFileBERT") - Notebooks
- Google Colab
- Kaggle
Rust DMHY Template Apply
Multi-core Rust implementation of the DMHY template recipe apply stage.
Build template recipes from the repository root:
cargo run --release --manifest-path tools\rust_dmhy_template_apply\Cargo.toml -- `
--cluster `
--input datasets\AnimeName\dmhy_list.jsonl `
--summary-output reports\dmhy_template_clusters.full_top5000.summary.json `
--samples-output reports\dmhy_template_clusters.full_top5000.samples.jsonl `
--clusters-output reports\dmhy_template_clusters.full_top5000.jsonl `
--recipes-output reports\dmhy_template_recipes.full_top5000.seed.jsonl `
--review-output reports\dmhy_template_review.full_top5000.jsonl `
--top 5000 `
--recipe-top 5000 `
--review-top 5000 `
--min-count 2 `
--recipe-min-count 25 `
--threads 24
Apply template recipes from the repository root:
cargo run --release --manifest-path tools\rust_dmhy_template_apply\Cargo.toml -- `
--input datasets\AnimeName\dmhy_list.jsonl `
--recipes reports\dmhy_template_recipes.full_top5000.seed.jsonl `
--output reports\dmhy_weak.template_generated.top5000.rust.jsonl `
--manifest-output reports\dmhy_weak.template_generated.top5000.rust.manifest.json
Audit low-frequency recipe output from the repository root:
cargo run --release --manifest-path tools\rust_dmhy_template_apply\Cargo.toml -- `
--audit-low-frequency `
--input datasets\AnimeName\dmhy_list.jsonl `
--recipes reports\dmhy_template_recipes.full_top5000.seed.jsonl `
--audit-output reports\dmhy_low_frequency_audit.rust.jsonl `
--audit-max-count 50 `
--threads 24
Verify the generated training output has no low-frequency blocking warnings:
cargo run --release --manifest-path tools\rust_dmhy_template_apply\Cargo.toml -- `
--verify-generated-output `
--input reports\dmhy_weak.template_generated.top5000.rust.jsonl `
--recipes reports\dmhy_template_recipes.full_top5000.seed.jsonl `
--audit-max-count 50
Review ambiguous title-boundary decisions with a local LM Studio model or with Pi RPC mode. This is an optional final-pass reviewer: it writes a decision cache JSONL and does not modify recipes or training data directly.
cargo run --release --manifest-path tools\rust_dmhy_template_apply\Cargo.toml -- `
--review-title-boundaries-lmstudio `
--recipes reports\dmhy_template_recipes.full_top5000.seed.jsonl `
--title-boundary-decisions-output reports\dmhy_title_boundary_lmstudio_decisions.jsonl `
--lmstudio-base-url http://127.0.0.1:1234/v1 `
--lmstudio-model qwen `
--title-boundary-min-similarity 0.25 `
--title-boundary-max-similarity 0.85 `
--limit 50
cargo run --release --manifest-path tools\rust_dmhy_template_apply\Cargo.toml -- `
--review-title-boundaries-pi-rpc `
--recipes reports\dmhy_template_recipes.full_top5000.seed.jsonl `
--title-boundary-decisions-output reports\dmhy_title_boundary_pi_rpc_decisions.jsonl `
--pi-rpc-model openai/gpt-5.5 `
--title-boundary-min-similarity 0.25 `
--title-boundary-max-similarity 0.85 `
--threads 4 `
--limit 50
For stratified audits, select heuristic strata explicitly:
# High-similarity adjacent-title merges. The old high-similarity keep heuristic
# was removed after Pi RPC review found it over-preserved title continuations.
cargo run --release --manifest-path tools\rust_dmhy_template_apply\Cargo.toml -- `
--review-title-boundaries-pi-rpc `
--recipes reports\dmhy_template_recipes.full_top5000.seed.jsonl `
--title-boundary-decisions-output reports\dmhy_title_boundary_pi_rpc_adjacent_highsim.jsonl `
--title-boundary-heuristic-contains merge_adjacent_title_continuation `
--title-boundary-min-similarity 0.85 `
--title-boundary-max-similarity 1.0 `
--threads 4 `
--limit 20
# Structural keep candidates normally excluded from ambiguous merge review.
cargo run --release --manifest-path tools\rust_dmhy_template_apply\Cargo.toml -- `
--review-title-boundaries-pi-rpc `
--recipes reports\dmhy_template_recipes.full_top5000.seed.jsonl `
--title-boundary-decisions-output reports\dmhy_title_boundary_pi_rpc_structural_keep.jsonl `
--title-boundary-heuristic-contains keep_structural_boundary `
--title-boundary-min-similarity 0.0 `
--title-boundary-max-similarity 1.0 `
--threads 4 `
--limit 20
Optional controls:
--threads 24
--limit 50000
--limit-templates 1000
--min-count 10
--confidence high
--expand sample --sample-per-template 100
--keep-encoding-noise
The output record schema is filename, tokens, labels, template_id, and
template, plus optional source_filename, path_trimmed, and
dropped_title_candidate_positions. Clustered recipe rows also include
title_spans and title_boundary_decisions metadata so downstream synthetic
augmentation can distinguish one logical title span from repeated/path title
slots.
For low-frequency templates (count <= --audit-max-count, default 50), apply
uses a conservative gate: records with no_title, multiple_title_spans,
path_retained, or hash_labeled audit warnings are skipped from the training
JSONL and left in the audit/review files. This keeps common templates stable
while preventing rare ambiguous path/title cases from polluting the generated
dataset.