task_categories:text-classification region:us
---
task_categories:
- text-classification

---
# AutoTrain Dataset for project: full2

## Dataset Description

This dataset has been automatically processed by AutoTrain for project full2.

### Languages

The BCP-47 code for the dataset's language is unk.

## Dataset Structure

### Data Instances

A sample from this dataset looks as follows:

```json
[
  {
    "text": "# TPQN\t4\n# Key\t0\tC\tmajor\t0\n# Meter\t0\t4\t4\t16\n# Meter\t292\t0\t0\t0\n# SPQN 0 0.5\n0 1 15 G#6\n1 1 19 C#4\n2 5 21 G#4\n3 7 19 C#5\n4 9 17 E5\n5 11 59 G#5\n6 13 50 A6\n7 13 43 C#6\n8 15 17 G#6\n9 17 39 E5\n10 17 44 G#6\n11 19 31 F#6\n12 19 21 C#5\n13 19 25 C#4\n14 21 60 G#4\n15 21 37 C#5\n16 23 27 E4\n17 25 58 C#4\n18 27 60 E4\n19 29 60 E6\n20 31 33 F#6\n21 33 60 F#6\n22 33 58 A3\n23 35 60 A4\n24 37 58 C#5\n25 39 52 E5\n26 41 48 A5\n27 43 46 C#6\n28 45 58 G#6\n29 47 56 C#6\n30 49 54 A5\n31 50 74 A6\n32 52 58 E5\n33 54 66 A5\n34 56 95 C#6\n35 58 75 G#6\n36 60 64 F#6\n37 62 65 E6\n38 64 66 F#6\n39 64 100 B3\n40 65 68 E6\n41 66 100 F#4\n42 66 70 F#6\n43 68 100 B4\n44 68 100 E6\n45 70 100 Eb5\n46 70 93 F#6\n47 71 73 F#5\n48 73 100 F#5\n49 74 79 B5\n50 75 77 G#6\n51 77 79 G#6\n52 77 79 Eb6\n53 79 80 Eb6\n54 79 91 G#6\n55 79 85 B5\n56 80 83 Eb6\n57 81 83 A6\n58 83 85 A6\n60 83 107 Eb6\n61 85 89 A6\n62 85 100 B5\n63 87 100 B6\n64 89 103 A6\n65 91 95 G#6\n66 93 97 F#6\n68 95 98 G#6\n69 97 102 F#6\n70 97 140 Eb4\n71 98 99 G#6\n72 99 100 G#6\n73 99 140 G#4\n74 100 102 G#6\n75 101 140 C5\n76 102 140 F#6\n77 102 104 G#6\n78 103 104 A6\n79 104 140 G#6\n80 105 106 A6\n81 105 113 G#5\n82 106 107 A6\n83 107 108 A6\n84 107 115 C6\n85 108 140 A6\n86 109 118 Eb6\n87 111 112 C7\n88 112 113 C7\n89 113 140 G#5\n90 113 142 C7\n91 115 131 C6\n92 118 123 Eb6\n93 120 160 C#7\n94 123 127 Eb6\n95 127 153 Eb6\n96 131 179 C6\n97 146 180 C#5\n98 146 147 E7\n99 147 172 E7\n100 150 190 G#6\n101 150 180 G#5\n102 153 155 Eb7\n103 153 180 Eb6\n104 155 157 Eb7\n105 157 176 Eb7\n106 160 179 C#7\n107 160 183 C#6\n108 164 212 G#7\n109 168 216 F#7\n110 172 174 E7\n111 174 178 E7\n112 176 178 Eb7\n114 178 181 E7\n115 179 220 Eb7\n116 180 181 C#7\n117 180 187 E5\n118 181 182 C#7\n119 181 220 E7\n120 182 183 C#7\n121 183 220 C#7\n122 183 184 A6\n123 183 220 C#6\n124 184 187 A6\n125 187 194 E5\n126 187 204 A6\n127 190 194 F#6\n128 190 194 A5\n129 194 201 E5\n130 194 206 F#6\n131 194 220 A5\n132 198 220 A4\n133 201 249 E5\n134 202 242 G#6\n135 204 205 A6\n136 205 207 A6\n137 206 229 B4\n138 206 208 F#6\n139 207 241 A6\n140 208 210 F#6\n141 210 212 F#5\n142 210 237 F#6\n143 212 217 F#5\n144 214 215 Eb6\n145 214 243 B5\n146 215 216 Eb6\n147 216 254 Eb6\n148 217 222 F#5\n149 221 260 Eb5\n150 222 224 F#5\n151 224 226 F#5\n152 226 260 F#5\n153 229 246 B4\n154 234 250 E6\n155 237 246 F#6\n156 240 288 C5\n157 241 289 G#5\n158 241 260 C6\n159 242 287 G#6\n160 246 247 F#6\n161 248 292 F#6\n162 250 286 E6\n163 255 260 Eb6\n164 260 280 C6\n165 260 280 Eb6\n The above is a music score for piano. TQDN 4 refers to \u201cTick per quarter note (QN) is 4\u201d; \u201c# Key\t0\tC\tmajor\t 0\u201d refers to \u201cKey defined at tick 0 is C major with key_fifth = 0. (key_fifth = 1 (one sharp), -1 (one flat), \u2026)\u201d; \u201c# Meter\t0\t4\t4\t16\u201d refers to \u201cMeter defined at tick 0 is 4/4, for which the bar length is 16 ticks.\u201d; \u201c# SPQN\t0\t0.5\u201d refers to \u201cTempo defined at tick 0 is 0.5 (sec per QN)\u201d. The note event is defined as [No.] [onset score time] [offset score time] [spelled pitch] According to the score, please predict its corresponding emotion label. The output shall be either one of these labels:happy, angry, sad, relaxed.",
    "target": 3
  },
  {
    "text": "# TPQN\t4\n# Key\t0\tC\tmajor\t0\n# Meter\t0\t4\t4\t16\n# Meter\t240\t0\t0\t0\n# SPQN 0 0.5\n0 4 22 C#5\n1 5 22 C5\n2 6 14 Bb4\n3 6 22 Bb2\n4 8 21 F3\n5 10 22 Bb3\n6 12 15 F4\n7 12 17 C4\n8 14 17 Bb4\n9 14 22 C#4\n10 15 22 F4\n11 17 22 C4\n12 17 22 Bb4\n13 19 22 F5\n14 21 36 Eb5\n15 21 23 F3\n16 21 36 F2\n17 21 36 G#4\n18 23 36 C3\n19 25 36 F3\n20 27 36 G3\n21 29 36 G#3\n22 32 36 C#5\n23 34 36 C5\n24 36 43 Bb4\n25 36 51 F#2\n26 36 42 C#4\n27 36 43 F#4\n28 38 51 C#3\n29 40 51 F#3\n30 42 47 G#3\n31 42 45 C#4\n32 43 47 Bb4\n33 43 47 F#4\n34 43 51 Bb3\n35 45 51 C#4\n36 47 51 G#3\n37 47 51 Bb4\n38 47 51 F#4\n39 49 51 F5\n40 51 66 Eb5\n41 51 66 Bb4\n42 51 66 F#4\n43 51 66 Eb2\n44 53 66 Bb2\n45 55 66 Eb3\n46 57 62 F3\n47 58 64 F#3\n48 62 66 F3\n49 62 66 C#5\n50 64 66 F#3\n51 64 66 C5\n52 66 71 C#4\n53 66 81 Bb4\n54 66 81 F#2\n55 66 77 F#4\n56 68 77 C#3\n57 69 81 F#3\n58 71 77 G#3\n59 71 73 C#4\n60 71 77 C5\n61 73 79 C#4\n62 73 79 Bb3\n63 73 81 C#5\n64 77 81 C5\n65 77 81 G#3\n66 77 81 F#4\n67 77 81 C#3\n68 79 81 Bb3\n69 79 81 C#4\n70 81 93 G#4\n71 81 96 G#2\n72 81 88 C#5\n73 83 96 Eb3\n74 84 96 G#3\n75 84 91 C5\n76 84 96 Eb4\n77 86 92 Bb3\n78 88 96 C#5\n79 88 93 C4\n80 90 96 Eb5\n81 92 96 C5\n82 92 96 Bb3\n83 93 96 G#4\n84 93 96 C4\n85 96 111 Bb2\n86 96 126 Bb4\n87 97 113 F3\n88 99 107 Bb3\n89 101 109 C4\n90 103 118 C#4\n91 103 119 F4\n92 107 115 Bb3\n93 109 117 C4\n94 111 126 Bb2\n95 113 126 F3\n96 115 122 Bb3\n97 117 120 C4\n98 119 126 C#4\n99 119 124 F4\n100 120 126 C4\n101 122 126 Bb3\n102 124 127 C#5\n103 124 126 F4\n104 126 127 C5\n105 126 141 Bb2\n106 126 133 Bb4\n107 128 141 F3\n108 130 141 Bb3\n109 132 135 F4\n110 132 137 C4\n111 133 137 Bb4\n112 133 141 C#4\n113 135 141 F4\n114 137 141 C4\n115 137 141 Bb4\n116 138 141 F5\n117 141 156 F2\n118 141 156 Eb5\n119 141 156 G#4\n120 142 156 C3\n121 144 156 F3\n122 146 156 G3\n123 148 152 G#3\n124 150 156 F4\n125 151 156 C#5\n126 152 156 G#3\n127 153 156 C5\n128 155 170 F#2\n129 155 162 F#4\n130 155 162 Bb4\n131 157 170 C#3\n132 159 162 F#3\n133 160 164 C#4\n134 160 166 G#3\n135 162 166 Bb4\n136 162 170 Bb3\n137 162 170 F#4\n138 164 170 C#4\n139 166 170 Bb4\n140 166 170 G#3\n141 168 170 F5\n142 170 185 Eb2\n143 170 185 Bb4\n144 170 185 Eb5\n145 170 185 F#4\n146 170 185 Eb4\n147 172 185 Bb2\n148 173 185 Eb3\n149 175 181 F3\n150 177 183 F#3\n151 181 185 C#5\n152 181 185 F3\n153 183 185 F#3\n154 183 185 C5\n155 185 200 Bb4\n156 185 191 C#4\n157 185 191 F#2\n158 185 191 F#4\n159 187 200 C#3\n160 189 191 F#3\n161 191 196 G#3\n162 191 196 C5\n163 191 192 C#4\n164 191 200 F#4\n165 193 198 Bb3\n166 193 200 C#5\n167 193 198 C#4\n168 196 200 G#3\n169 196 200 C5\n170 198 200 Bb3\n171 198 200 C#4\n172 200 212 G#4\n173 200 215 G#2\n174 200 207 C#5\n175 202 215 Eb3\n176 203 215 G#3\n177 204 211 C5\n178 204 215 Eb4\n179 205 211 Bb3\n180 207 215 C#5\n181 207 212 C4\n182 209 215 Eb5\n183 211 215 C5\n184 211 215 Bb3\n185 212 215 G#4\n186 212 215 C4\n187 215 240 Bb4\n188 215 230 Bb2\n189 217 230 F3\n190 219 226 Bb3\n191 220 223 C4\n192 222 230 C#4\n193 223 238 F4\n194 223 228 C4\n195 226 234 Bb3\n196 228 230 C4\n197 230 240 Bb2\n198 232 240 F3\n199 234 240 Bb3\n200 236 239 C4\n201 238 240 C#4\n202 238 240 F4\n The above is a music score for piano. TQDN 4 refers to \u201cTick per quarter note (QN) is 4\u201d; \u201c# Key\t0\tC\tmajor\t 0\u201d refers to \u201cKey defined at tick 0 is C major with key_fifth = 0. (key_fifth = 1 (one sharp), -1 (one flat), \u2026)\u201d; \u201c# Meter\t0\t4\t4\t16\u201d refers to \u201cMeter defined at tick 0 is 4/4, for which the bar length is 16 ticks.\u201d; \u201c# SPQN\t0\t0.5\u201d refers to \u201cTempo defined at tick 0 is 0.5 (sec per QN)\u201d. The note event is defined as [No.] [onset score time] [offset score time] [spelled pitch] According to the score, please predict its corresponding emotion label. The output shall be either one of these labels:happy, angry, sad, relaxed.",
    "target": 0
  }
]
```

### Dataset Fields

The dataset has the following fields (also called "features"):

```json
{
  "text": "Value(dtype='string', id=None)",
  "target": "ClassLabel(names=['angry', 'happy', 'relaxed', 'sad'], id=None)"
}
```

### Dataset Splits

This dataset is split into a train and validation split. The split sizes are as follow:

| Split name   | Num samples         |
| ------------ | ------------------- |
| train        | 855 |
| valid        | 216 |
