Token Classification
Transformers
ONNX
Safetensors
English
Japanese
Chinese
bert
anime
filename-parsing
Eval Results (legacy)
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: | |
| ```powershell | |
| 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: | |
| ```powershell | |
| 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: | |
| ```powershell | |
| 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: | |
| ```powershell | |
| 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. | |
| ```powershell | |
| 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 | |
| ``` | |
| ```powershell | |
| 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: | |
| ```powershell | |
| # 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: | |
| ```powershell | |
| --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. | |