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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ModerRAS/AniFileBERT") model = AutoModelForTokenClassification.from_pretrained("ModerRAS/AniFileBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Clarify generated training artifacts
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docs/training.md
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@@ -99,18 +99,15 @@ IDs and records any `[UNK]` coverage in the manifest.
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## 5. Current Schema v2 Cache-First Training / 当前 schema v2 cache-first 训练
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The current published checkpoint is the repaired hard-focus schema v2 run
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仓库根目录 artifact 与该 final checkpoint 一致。
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本节里的 hard-focus JSONL 和 encoded-cache 目录都是 ignored `data/` 路径下的本地生成物;
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复现训练前需要重新生成。
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```powershell
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cargo run --release --manifest-path tools\encoded_dataset_cache\Cargo.toml -- `
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## 5. Current Schema v2 Cache-First Training / 当前 schema v2 cache-first 训练
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The current published checkpoint is the repaired hard-focus schema v2 run
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recorded in `reports/training_lineage.json`. The repository root artifacts are
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the release surface. Local `checkpoints/`, hard-focus JSONL, and encoded-cache
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directories are generated artifacts under ignored paths; rebuild or restore
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them before reproducing the exact training run.
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当前发布 checkpoint 是 `reports/training_lineage.json` 记录的 repaired hard-focus
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schema v2 run。仓库根目录 artifact 是发布面。本地 `checkpoints/`、hard-focus JSONL
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和 encoded-cache 目录都是 ignored 路径下的生成物;复现训练前需要重新生成或恢复。
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```powershell
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cargo run --release --manifest-path tools\encoded_dataset_cache\Cargo.toml -- `
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