Release 0.6.1

evaluate/src/main.rs

@@ -157,10 +157,10 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
             let precision = f64::from(n_tp) / f64::from(n_tp + n_fp);
             let recall = f64::from(n_tp) / f64::from(n_tp + n_fn);
             let f1 = 2. * precision * recall / (precision + recall);
-            println!("Precision: {}", precision);
-            println!("Recall: {}", recall);
-            println!("F1: {}", f1);
-            println!("TP: {}, TN: {}, FP: {}, FN: {}", n_tp, n_tn, n_fp, n_fn);
+            println!("Precision: {precision}");
+            println!("Recall: {recall}");
+            println!("F1: {f1}");
+            println!("TP: {n_tp}, TN: {n_tn}, FP: {n_fp}, FN: {n_fn}");
         }
         EvaluationMetric::Word => {
             // Reference:
@@ -201,9 +201,9 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
             let precision = f64::from(n_cor) / f64::from(n_sys);
             let recall = f64::from(n_cor) / f64::from(n_ref);
             let f1 = 2. * precision * recall / (precision + recall);
-            println!("Precision: {}", precision);
-            println!("Recall: {}", recall);
-            println!("F1: {}", f1);
+            println!("Precision: {precision}");
+            println!("Recall: {recall}");
+            println!("F1: {f1}");
         }
     }
 

predict/src/main.rs

@@ -67,7 +67,7 @@ fn print_scores(s: &Sentence, out: &mut dyn Write) -> Result<(), Box<dyn std::er
     let mut chars_iter = s.as_raw_text().chars();
     let mut prev_c = chars_iter.next().unwrap();
     for (i, (c, score)) in chars_iter.zip(s.boundary_scores()).enumerate() {
-        writeln!(out, "{}:{}{} {}", i, prev_c, c, score)?;
+        writeln!(out, "{i}:{prev_c}{c} {score}")?;
         prev_c = c;
     }
     writeln!(out)?;
@@ -116,7 +116,7 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
                     s.fill_tags();
                 }
                 s.write_tokenized_text(&mut buf);
-                writeln!(out, "{}", buf)?;
+                writeln!(out, "{buf}")?;
                 if args.scores {
                     print_scores(&s, &mut *out)?;
                 }
@@ -143,7 +143,7 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
                 s_orig.boundaries_mut().copy_from_slice(s.boundaries());
                 s_orig.tags_mut().clone_from_slice(s.tags());
                 s_orig.write_tokenized_text(&mut buf);
-                writeln!(out, "{}", buf)?;
+                writeln!(out, "{buf}")?;
                 if args.scores {
                     print_scores(&s, &mut *out)?;
                 }

train/src/main.rs

@@ -77,12 +77,12 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
     let mut train_sents = vec![];
 
     for path in args.tok {
-        eprintln!("Loading {:?} ...", path);
+        eprintln!("Loading {path:?} ...");
         let f = File::open(path)?;
         let f = BufReader::new(f);
         for (i, line) in f.lines().enumerate() {
             if i % 10000 == 0 {
-                eprint!("# of sentences: {}\r", i);
+                eprint!("# of sentences: {i}\r");
                 stderr().flush()?;
             }
             let s = Sentence::from_tokenized(&line?)?;
@@ -101,12 +101,12 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
         eprintln!("# of sentences: {}", train_sents.len());
     }
     for path in args.part {
-        eprintln!("Loading {:?} ...", path);
+        eprintln!("Loading {path:?} ...");
         let f = File::open(path)?;
         let f = BufReader::new(f);
         for (i, line) in f.lines().enumerate() {
             if i % 10000 == 0 {
-                eprint!("# of sentences: {}\r", i);
+                eprint!("# of sentences: {i}\r");
                 stderr().flush()?;
             }
             let s = Sentence::from_partial_annotation(&line?)?;
@@ -128,7 +128,7 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
     let mut tag_dictionary = vec![];
     let mut dictionary = BTreeSet::new();
     for path in args.dict {
-        eprintln!("Loading {:?} ...", path);
+        eprintln!("Loading {path:?} ...");
         let f = File::open(path)?;
         let f = BufReader::new(f);
         for line in f.lines() {

vaporetto/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vaporetto"
-version = "0.6.0"
+version = "0.6.1"
 edition = "2021"
 rust-version = "1.60"
 authors = ["Koichi Akabe <[email protected]>"]

vaporetto/src/sentence.rs

@@ -584,7 +584,7 @@ impl<'a, 'b> Sentence<'a, 'b> {
                     } else {
                         return Err(VaporettoError::invalid_argument(
                             "partial_annotation_text",
-                            format!("contains an invalid boundary character: '{}'", c),
+                            format!("contains an invalid boundary character: '{c}'"),
                         ));
                     }
                 }

vaporetto/src/tag_trainer.rs

@@ -181,7 +181,7 @@ impl<'a> TagTrainer<'a> {
 
             let mut builder = liblinear::Builder::new();
             let training_input = liblinear::util::TrainingInput::from_sparse_features(ys, xs)
-                .map_err(|e| VaporettoError::invalid_model(format!("liblinear error: {:?}", e)))?;
+                .map_err(|e| VaporettoError::invalid_model(format!("liblinear error: {e:?}")))?;
             builder.problem().input_data(training_input).bias(1.0);
             builder
                 .parameters()
@@ -321,9 +321,9 @@ impl<'a> TagTrainer<'a> {
                 cost,
                 solver,
             )?);
-            eprint!("Tags: {}/{}\r", i, n_tokens);
+            eprint!("Tags: {i}/{n_tokens}\r");
         }
-        eprintln!("Tags: {}/{}", n_tokens, n_tokens);
+        eprintln!("Tags: {n_tokens}/{n_tokens}");
         liblinear::toggle_liblinear_stdout_output(true);
         Ok(tag_models)
     }

vaporetto/src/trainer.rs

@@ -352,7 +352,7 @@ impl<'a> Trainer<'a> {
     pub fn train(self, epsilon: f64, cost: f64, solver: SolverType) -> Result<Model> {
         let mut builder = liblinear::Builder::new();
         let training_input = liblinear::util::TrainingInput::from_sparse_features(self.ys, self.xs)
-            .map_err(|e| VaporettoError::invalid_model(format!("liblinear error: {:?}", e)))?;
+            .map_err(|e| VaporettoError::invalid_model(format!("liblinear error: {e:?}")))?;
         builder.problem().input_data(training_input).bias(1.0);
         builder
             .parameters()

vaporetto_rules/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vaporetto_rules"
-version = "0.6.0"
+version = "0.6.1"
 edition = "2021"
 rust-version = "1.60"
 authors = ["Koichi Akabe <[email protected]>"]
@@ -15,7 +15,7 @@ categories = ["text-processing", "no-std"]
 [dependencies]
 hashbrown = "0.12.3"  # MIT or Apache-2.0
 unicode-segmentation = "1.10.0"  # MIT or Apache-2.0
-vaporetto = { path = "../vaporetto", version = "=0.6.0", default-features = false, features = ["alloc"] }  # MIT or Apache-2.0
+vaporetto = { path = "../vaporetto", version = "=0.6.1", default-features = false, features = ["alloc"] }  # MIT or Apache-2.0
 
 [dev-dependencies]
-vaporetto = { path = "../vaporetto", version = "=0.6.0" }  # MIT or Apache-2.0
+vaporetto = { path = "../vaporetto", version = "=0.6.1" }  # MIT or Apache-2.0

vaporetto_tantivy/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vaporetto_tantivy"
-version = "0.6.0"
+version = "0.6.1"
 edition = "2021"
 rust-version = "1.60"
 authors = ["Koichi Akabe <[email protected]>"]
@@ -13,8 +13,8 @@ keywords = ["japanese", "tokenizer", "tantivy"]
 categories = ["text-processing"]
 
 [dependencies]
-vaporetto = { path = "../vaporetto", version = "=0.6.0" }  # MIT or Apache-2.0
-vaporetto_rules = { path = "../vaporetto_rules", version = "=0.6.0" }  # MIT or Apache-2.0
+vaporetto = { path = "../vaporetto", version = "=0.6.1" }  # MIT or Apache-2.0
+vaporetto_rules = { path = "../vaporetto_rules", version = "=0.6.1" }  # MIT or Apache-2.0
 tantivy = "0.18"  # MIT
 
 [dev-dependencies]