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
Use direct local HTTP for LM Studio reviewer
Browse files
tools/rust_dmhy_template_apply/README.md
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@@ -62,7 +62,7 @@ cargo run --release --manifest-path tools\rust_dmhy_template_apply\Cargo.toml --
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--review-title-boundaries-lmstudio `
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--recipes reports\dmhy_template_recipes.full_top5000.seed.jsonl `
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--title-boundary-decisions-output reports\dmhy_title_boundary_lmstudio_decisions.jsonl `
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--lmstudio-base-url http://
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--lmstudio-model qwen `
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--title-boundary-min-similarity 0.25 `
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--title-boundary-max-similarity 0.85 `
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--review-title-boundaries-lmstudio `
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--recipes reports\dmhy_template_recipes.full_top5000.seed.jsonl `
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--title-boundary-decisions-output reports\dmhy_title_boundary_lmstudio_decisions.jsonl `
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+
--lmstudio-base-url http://127.0.0.1:1234/v1 `
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--lmstudio-model qwen `
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--title-boundary-min-similarity 0.25 `
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--title-boundary-max-similarity 0.85 `
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tools/rust_dmhy_template_apply/src/main.rs
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@@ -64,7 +64,7 @@ struct Args {
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default_value = "reports/dmhy_title_boundary_lmstudio_decisions.jsonl"
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)]
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title_boundary_decisions_output: PathBuf,
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#[arg(long, default_value = "http://
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lmstudio_base_url: String,
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#[arg(long, default_value = "qwen")]
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lmstudio_model: String,
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fn post_json_http(endpoint: &str, body: &Value, timeout_seconds: u64) -> Result<Value> {
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let (host, port, path) = parse_http_endpoint(endpoint)?;
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let host_port = format!("{host}:{port}");
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let mut stream = std::net::TcpStream::connect(
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.with_context(|| format!("failed to connect to {host_port}"))?;
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let timeout = std::time::Duration::from_secs(timeout_seconds);
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stream.set_read_timeout(Some(timeout))?;
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default_value = "reports/dmhy_title_boundary_lmstudio_decisions.jsonl"
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)]
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title_boundary_decisions_output: PathBuf,
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#[arg(long, default_value = "http://127.0.0.1:1234/v1")]
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lmstudio_base_url: String,
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#[arg(long, default_value = "qwen")]
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lmstudio_model: String,
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fn post_json_http(endpoint: &str, body: &Value, timeout_seconds: u64) -> Result<Value> {
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let (host, port, path) = parse_http_endpoint(endpoint)?;
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let host_port = format!("{host}:{port}");
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let mut stream = std::net::TcpStream::connect(&host_port)
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.with_context(|| format!("failed to connect to {host_port}"))?;
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let timeout = std::time::Duration::from_secs(timeout_seconds);
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stream.set_read_timeout(Some(timeout))?;
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