Upload folder using huggingface_hub
Browse files- README.md +62 -0
- config.json +4841 -0
- vocab.json +0 -0
- weights.pt +3 -0
README.md
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| 1 |
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---
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language:
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- sin
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- pli
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- san
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- en
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- ta
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- hi
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- bn
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- ar
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- fr
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- de
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license: cc-by-4.0
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tags:
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- language-identification
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- fasttext
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- continual-learning
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- leaf-surgery
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- sinhala
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- pali
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- sanskrit
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datasets:
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- script-langid/sinhala-pali-sanskrit-target
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metrics:
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- f1
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- accuracy
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---
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# FastText Continual Leaf Surgery (11-Language Rehearsal)
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This model is a continual fine-tuning of Meta's `fastText LID-176` utilizing **Hierarchical Softmax Leaf Surgery**.
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## Architecture & Innovation
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- **Base Architecture**: Meta `fastText LID-176` (100-dim dense subword embeddings with Huffman tree hierarchical softmax).
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- **Leaf Surgery**: Instead of retraining the classification head from scratch (which shuffles the Huffman tree and causes catastrophic forgetting of 176 pre-trained languages), the original hierarchical softmax binary decision paths are preserved. The Sinhala (`si`) leaf node is surgically split into an internal decision node branching into modern Sinhala and canonical Pali, with Sanskrit adaptation.
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- **Continual Rehearsal**: Trained on the 11-language uniform rehearsal buffer (`train_11lang_uniform.csv`) balancing the 3 target languages in Sinhala script with 8 global and regional anchor languages (English, Tamil, Hindi, Bengali, Arabic, French, German, and Sanskrit in Devanagari).
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## Benchmark Performance
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| Benchmark | Macro F1 | Sinhala F1 | Pali F1 | Sanskrit F1 | Overall Accuracy |
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|---|:---:|:---:|:---:|:---:|:---:|
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| **WiLI-2018 (11-Lang)** | **0.9811** | 0.9766 | 0.9881 | 0.9895 | **98.00%** |
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| **CommonLID (11-Lang)** | **0.9486** | 0.9766 | 0.9881 | 0.9695 | **96.82%** |
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| **FLORES+ (11-Lang)** | **0.9395** | 0.9766 | 0.9881 | 0.9817 | **92.71%** |
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## How to Load in Python
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```python
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from data_pipeline.fasttext_continual.model import ContinualLID
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# Loads config.json, vocab.json, and weights.pt automatically from Hugging Face
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model = ContinualLID.from_pretrained("script-langid/fasttext-leaf-surgery-11lang")
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# Inference example:
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text = "නමෝ තස්ස භගවතෝ අරහතෝ සම්මා සම්බුද්ධස්ස"
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prediction = model.predict(text)
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print(prediction) # 'pi' (Pali)
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```
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## Repository & Research
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- **GitHub**: [Sinhala-Script-Language-Identification-LangID-for-Sinhala-Pali-and-Sanskrit](https://github.com/Maleesha-K/Sinhala-Script-Language-Identification-LangID-for-Sinhala-Pali-and-Sanskrit)
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- **Research**: University of Moratuwa, Department of Computer Science & Engineering.
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config.json
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|
|
| 1 |
+
{
|
| 2 |
+
"format_version": 1,
|
| 3 |
+
"architecture": "fasttext_hierarchical_softmax",
|
| 4 |
+
"args": {
|
| 5 |
+
"dim": 16,
|
| 6 |
+
"minn": 2,
|
| 7 |
+
"maxn": 4,
|
| 8 |
+
"bucket": 2000000,
|
| 9 |
+
"wordNgrams": 1,
|
| 10 |
+
"label": "__label__"
|
| 11 |
+
},
|
| 12 |
+
"labels": [
|
| 13 |
+
"en",
|
| 14 |
+
"ru",
|
| 15 |
+
"de",
|
| 16 |
+
"fr",
|
| 17 |
+
"it",
|
| 18 |
+
"ja",
|
| 19 |
+
"es",
|
| 20 |
+
"ceb",
|
| 21 |
+
"tr",
|
| 22 |
+
"pt",
|
| 23 |
+
"uk",
|
| 24 |
+
"eo",
|
| 25 |
+
"pl",
|
| 26 |
+
"sv",
|
| 27 |
+
"nl",
|
| 28 |
+
"he",
|
| 29 |
+
"zh",
|
| 30 |
+
"hu",
|
| 31 |
+
"ar",
|
| 32 |
+
"ca",
|
| 33 |
+
"fi",
|
| 34 |
+
"cs",
|
| 35 |
+
"fa",
|
| 36 |
+
"sr",
|
| 37 |
+
"el",
|
| 38 |
+
"vi",
|
| 39 |
+
"bg",
|
| 40 |
+
"ko",
|
| 41 |
+
"no",
|
| 42 |
+
"mk",
|
| 43 |
+
"ro",
|
| 44 |
+
"id",
|
| 45 |
+
"th",
|
| 46 |
+
"hy",
|
| 47 |
+
"da",
|
| 48 |
+
"ta",
|
| 49 |
+
"hi",
|
| 50 |
+
"hr",
|
| 51 |
+
"sh",
|
| 52 |
+
"be",
|
| 53 |
+
"ka",
|
| 54 |
+
"te",
|
| 55 |
+
"kk",
|
| 56 |
+
"war",
|
| 57 |
+
"lt",
|
| 58 |
+
"gl",
|
| 59 |
+
"sk",
|
| 60 |
+
"bn",
|
| 61 |
+
"eu",
|
| 62 |
+
"sl",
|
| 63 |
+
"kn",
|
| 64 |
+
"ml",
|
| 65 |
+
"mr",
|
| 66 |
+
"et",
|
| 67 |
+
"az",
|
| 68 |
+
"ms",
|
| 69 |
+
"sq",
|
| 70 |
+
"la",
|
| 71 |
+
"bs",
|
| 72 |
+
"nn",
|
| 73 |
+
"ur",
|
| 74 |
+
"lv",
|
| 75 |
+
"my",
|
| 76 |
+
"tt",
|
| 77 |
+
"af",
|
| 78 |
+
"oc",
|
| 79 |
+
"nds",
|
| 80 |
+
"ky",
|
| 81 |
+
"ast",
|
| 82 |
+
"tl",
|
| 83 |
+
"is",
|
| 84 |
+
"ia",
|
| 85 |
+
"si",
|
| 86 |
+
"gu",
|
| 87 |
+
"km",
|
| 88 |
+
"br",
|
| 89 |
+
"ba",
|
| 90 |
+
"uz",
|
| 91 |
+
"bo",
|
| 92 |
+
"pa",
|
| 93 |
+
"vo",
|
| 94 |
+
"als",
|
| 95 |
+
"ne",
|
| 96 |
+
"cy",
|
| 97 |
+
"jbo",
|
| 98 |
+
"fy",
|
| 99 |
+
"mn",
|
| 100 |
+
"lb",
|
| 101 |
+
"ce",
|
| 102 |
+
"ug",
|
| 103 |
+
"tg",
|
| 104 |
+
"sco",
|
| 105 |
+
"sa",
|
| 106 |
+
"cv",
|
| 107 |
+
"jv",
|
| 108 |
+
"min",
|
| 109 |
+
"io",
|
| 110 |
+
"or",
|
| 111 |
+
"as",
|
| 112 |
+
"new",
|
| 113 |
+
"ga",
|
| 114 |
+
"mg",
|
| 115 |
+
"an",
|
| 116 |
+
"ckb",
|
| 117 |
+
"sw",
|
| 118 |
+
"bar",
|
| 119 |
+
"lmo",
|
| 120 |
+
"yi",
|
| 121 |
+
"arz",
|
| 122 |
+
"mhr",
|
| 123 |
+
"azb",
|
| 124 |
+
"sah",
|
| 125 |
+
"pnb",
|
| 126 |
+
"su",
|
| 127 |
+
"bpy",
|
| 128 |
+
"pms",
|
| 129 |
+
"ilo",
|
| 130 |
+
"wuu",
|
| 131 |
+
"ku",
|
| 132 |
+
"ps",
|
| 133 |
+
"ie",
|
| 134 |
+
"xmf",
|
| 135 |
+
"yue",
|
| 136 |
+
"gom",
|
| 137 |
+
"li",
|
| 138 |
+
"mwl",
|
| 139 |
+
"kw",
|
| 140 |
+
"sd",
|
| 141 |
+
"hsb",
|
| 142 |
+
"scn",
|
| 143 |
+
"gd",
|
| 144 |
+
"pam",
|
| 145 |
+
"bh",
|
| 146 |
+
"mai",
|
| 147 |
+
"vec",
|
| 148 |
+
"mt",
|
| 149 |
+
"dv",
|
| 150 |
+
"wa",
|
| 151 |
+
"mzn",
|
| 152 |
+
"am",
|
| 153 |
+
"qu",
|
| 154 |
+
"eml",
|
| 155 |
+
"cbk",
|
| 156 |
+
"tk",
|
| 157 |
+
"rm",
|
| 158 |
+
"os",
|
| 159 |
+
"vls",
|
| 160 |
+
"yo",
|
| 161 |
+
"lo",
|
| 162 |
+
"lez",
|
| 163 |
+
"so",
|
| 164 |
+
"myv",
|
| 165 |
+
"diq",
|
| 166 |
+
"mrj",
|
| 167 |
+
"dsb",
|
| 168 |
+
"frr",
|
| 169 |
+
"ht",
|
| 170 |
+
"gn",
|
| 171 |
+
"bxr",
|
| 172 |
+
"kv",
|
| 173 |
+
"sc",
|
| 174 |
+
"nah",
|
| 175 |
+
"krc",
|
| 176 |
+
"bcl",
|
| 177 |
+
"nap",
|
| 178 |
+
"gv",
|
| 179 |
+
"av",
|
| 180 |
+
"rue",
|
| 181 |
+
"xal",
|
| 182 |
+
"pfl",
|
| 183 |
+
"dty",
|
| 184 |
+
"hif",
|
| 185 |
+
"co",
|
| 186 |
+
"lrc",
|
| 187 |
+
"vep",
|
| 188 |
+
"tyv",
|
| 189 |
+
"pi"
|
| 190 |
+
],
|
| 191 |
+
"paths": [
|
| 192 |
+
[
|
| 193 |
+
172,
|
| 194 |
+
174
|
| 195 |
+
],
|
| 196 |
+
[
|
| 197 |
+
167,
|
| 198 |
+
171,
|
| 199 |
+
173,
|
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The diff for this file is too large to render.
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weights.pt
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@@ -0,0 +1,3 @@
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