spBLEU tokenizer, run on more languages

bibliography.bib

@@ -209,6 +209,22 @@
   keywords = {dataset,n=200}
 }
 
+@misc{petrovLanguageModelTokenizers2023,
+  title = {Language {{Model Tokenizers Introduce Unfairness Between Languages}}},
+  author = {Petrov, Aleksandar and Malfa, Emanuele La and Torr, Philip H. S. and Bibi, Adel},
+  year = {2023},
+  month = oct,
+  number = {arXiv:2305.15425},
+  eprint = {2305.15425},
+  primaryclass = {cs},
+  publisher = {arXiv},
+  doi = {10.48550/arXiv.2305.15425},
+  urldate = {2025-03-24},
+  abstract = {Recent language models have shown impressive multilingual performance, even when not explicitly trained for it. Despite this, there are concerns about the quality of their outputs across different languages. In this paper, we show how disparity in the treatment of different languages arises at the tokenization stage, well before a model is even invoked. The same text translated into different languages can have drastically different tokenization lengths, with differences up to 15 times in some cases. These disparities persist even for tokenizers that are intentionally trained for multilingual support. Character-level and byte-level models also exhibit over 4 times the difference in the encoding length for some language pairs. This induces unfair treatment for some language communities in regard to the cost of accessing commercial language services, the processing time and latency, as well as the amount of content that can be provided as context to the models. Therefore, we make the case that we should train future language models using multilingually fair subword tokenizers.},
+  archiveprefix = {arXiv},
+  file = {/Users/david/Zotero/storage/NX3DT98Z/Petrov et al. - 2023 - Language Model Tokenizers Introduce Unfairness Between Languages.pdf;/Users/david/Zotero/storage/FKJQP22D/2305.html}
+}
+
 @inproceedings{robinsonChatGPTMTCompetitive2023,
   title = {{{ChatGPT MT}}: {{Competitive}} for {{High-}} (but {{Not Low-}}) {{Resource Languages}}},
   shorttitle = {{{ChatGPT MT}}},

data/INFO

@@ -2,4 +2,4 @@ fleurs: https://huggingface.co/datasets/google/fleurs via eval.py
 floresp-v2.0-rc.3: https://github.com/openlanguagedata/flores
 glottolog_languoid.csv: https://glottolog.org/meta/downloads
 ScriptCodes.csv: https://www.unicode.org/iso15924/iso15924-codes.html
-spbleu: https://github.com/facebookresearch/flores/tree/main
+spbleu: https://github.com/facebookresearch/flores/tree/main/flores200#spm-and-dictionary

evals/main.py

@@ -13,7 +13,7 @@ from tqdm.asyncio import tqdm_asyncio
 # ===== config =====
 
 n_sentences = 30
-langs_eval = languages.iloc[:10]
+langs_eval = languages.iloc[:30]
 langs_eval_detailed = languages.iloc[:2]
 transcription_langs_eval = languages.iloc[:10]
 transcription_langs_eval_detailed = languages.iloc[:5]

evals/tasks.py

@@ -3,16 +3,16 @@ import random
 import evaluate
 import pandas as pd
 from joblib.memory import Memory
-from transformers import NllbTokenizer
 from languages import languages, script_name
 from datasets_.flores import flores_sentences
 from models import complete, transcribe
+import sentencepiece as spm
+
 cache = Memory(location=".cache", verbose=0).cache
 bleu = evaluate.load("bleu")
 chrf = evaluate.load("chrf")
 wer = evaluate.load("wer")
-tokenizer = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
-
+tokenizer = spm.SentencePieceProcessor(model_file="data/spbleu/flores200_sacrebleu_tokenizer_spm.model")
 
 # sample languages to translate to
 target_languages = languages[languages["in_benchmark"]].sample(

frontend/public/results.json

@@ -26,7 +26,7 @@
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@@ -39,9 +39,9 @@
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@@ -54,41 +54,41 @@
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-      "provider": "Amazon",
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+      "provider": "Meta Llama",
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@@ -101,7 +101,7 @@
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@@ -116,7 +116,7 @@
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@@ -129,11 +129,11 @@
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@@ -144,9 +144,9 @@
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@@ -161,7 +161,7 @@
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@@ -176,7 +176,7 @@
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@@ -220,7 +220,7 @@
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@@ -248,7 +248,7 @@
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@@ -262,7 +262,7 @@
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@@ -300,11 +300,11 @@
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@@ -314,11 +314,11 @@
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@@ -328,13 +328,13 @@
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@@ -342,13 +342,13 @@
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@@ -356,13 +356,13 @@
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@@ -370,13 +370,13 @@
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@@ -384,13 +384,13 @@
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@@ -398,13 +398,13 @@
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@@ -426,13 +426,13 @@
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@@ -440,13 +440,13 @@
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@@ -454,13 +454,13 @@
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@@ -482,13 +482,13 @@
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@@ -510,13 +510,13 @@
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@@ -524,13 +524,13 @@
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@@ -538,13 +538,13 @@
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@@ -552,13 +552,13 @@
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@@ -566,13 +566,13 @@
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@@ -10182,7 +10182,7 @@
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@@ -10217,7 +10217,7 @@
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@@ -10308,13 +10308,13 @@
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@@ -19473,19 +19473,19 @@
       ]
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           "name": "Turkish",
           "bcp_47": "tr",
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         {
           "name": "Polish",
@@ -19550,19 +19550,19 @@
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     "UZ": {
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           "bcp_47": "tr",
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           "name": "Uzbek",
@@ -19579,13 +19579,13 @@
       ]
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     "LI": {
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           "name": "German",
           "bcp_47": "de",
           "population": 39137,
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         },
         {
           "name": "Swiss German",
@@ -19602,19 +19602,19 @@
       ]
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     "JP": {
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           "bcp_47": "ja",
           "population": 119231650,
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         {
           "name": "Korean",
           "bcp_47": "ko",
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         {
           "name": "Central Okinawan",
@@ -19625,13 +19625,13 @@
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           "bcp_47": "fa",
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         {
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@@ -19684,13 +19684,13 @@
       ]
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         {
           "name": "Albanian",
@@ -19707,35 +19707,35 @@
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       "languages": [
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           "bcp_47": "ko",
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           "name": "Esperanto",
@@ -19746,13 +19746,13 @@
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           "bcp_47": "it",
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         {
           "name": "Latin",

results.json

@@ -3,29 +3,29 @@
     {
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@@ -41,7 +41,7 @@
       "model": "amazon/nova-micro-v1",
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@@ -69,7 +69,7 @@
       "model": "google/gemini-2.0-flash-001",
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-      "score": 0.9625160007216136,
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     {
@@ -97,7 +97,7 @@
       "model": "google/gemini-2.0-flash-lite-001",
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@@ -125,7 +125,7 @@
       "model": "google/gemma-3-27b-it",
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@@ -153,7 +153,7 @@
       "model": "meta-llama/llama-3-70b-instruct",
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@@ -181,14 +181,14 @@
       "model": "meta-llama/llama-3.1-70b-instruct",
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@@ -202,29 +202,29 @@
       "model": "meta-llama/llama-3.3-70b-instruct",
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@@ -237,7 +237,7 @@
       "model": "microsoft/phi-4-multimodal-instruct",
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@@ -265,7 +265,7 @@
       "model": "mistralai/mistral-nemo",
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@@ -293,7 +293,7 @@
       "model": "mistralai/mistral-small-24b-instruct-2501",
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@@ -321,7 +321,7 @@
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@@ -349,7 +349,7 @@
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@@ -701,7 +701,7 @@
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@@ -827,10 +827,58 @@
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+      "language_name": "Egyptian Arabic",
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@@ -1613,7 +1661,7 @@
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@@ -1992,7 +2040,7 @@
       "family": "Indo-European",
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       "fleurs_tag": "ca_es",
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@@ -2587,10 +2635,58 @@
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+      "language_name": "German",
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+      "flores_path": "deu_Latn",
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+      "autonym": "Deutsch",
+      "family": "Indo-European",
+      "flores_path": "deu_Latn",
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@@ -2925,7 +3021,7 @@
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@@ -2941,7 +3037,7 @@
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@@ -3080,7 +3176,7 @@
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@@ -3131,10 +3227,58 @@
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+      "language_name": "Persian",
+      "autonym": "فارسی",
+      "family": "Indo-European",
+      "flores_path": "pes_Arab",
+      "fleurs_tag": "fa_ir",
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+      "family": "Indo-European",
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+      "family": "Indo-European",
+      "flores_path": "pes_Arab",
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@@ -3243,10 +3387,58 @@
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+      "autonym": "Filipino",
+      "family": "Austronesian",
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@@ -3341,7 +3533,7 @@
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@@ -3544,7 +3736,7 @@
       "family": "Indo-European",
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@@ -3899,36 +4091,84 @@
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+      "bcp_47": "gu",
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+      "language_name": "Gujarati",
+      "autonym": "ગુજરાતી",
+      "family": "Indo-European",
+      "flores_path": "guj_Gujr",
+      "fleurs_tag": "gu_in",
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-      "autonym": "Wayuu",
-      "family": "Arawakan",
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+      "bcp_47": "gu",
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+      "language_name": "Gujarati",
+      "autonym": "ગુજરાતી",
+      "family": "Indo-European",
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@@ -4667,10 +4955,58 @@
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+      "language_name": "Italian",
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+      "flores_path": "ita_Latn",
+      "fleurs_tag": "it_it",
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+      "language_name": "Italian",
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+      "flores_path": "ita_Latn",
+      "fleurs_tag": "it_it",
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@@ -4715,10 +5051,58 @@
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+      "fleurs_tag": "ja_jp",
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+      "language_name": "Javanese",
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+      "language_name": "Georgian",
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@@ -4952,7 +5384,7 @@
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+      "language_name": "Korean",
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+      "flores_path": "kor_Hang",
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+      "flores_path": "kor_Hang",
+      "fleurs_tag": "ko_kr",
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+      "flores_path": "kor_Hang",
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@@ -5752,7 +6232,7 @@
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@@ -6872,7 +7352,7 @@
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@@ -7019,10 +7499,58 @@
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+      "language_name": "Marathi",
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+      "family": "Indo-European",
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+      "language_name": "Marathi",
+      "autonym": "मराठी",
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+      "flores_path": "mar_Deva",
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@@ -7976,7 +8504,7 @@
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@@ -8029,7 +8557,7 @@
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@@ -9224,7 +9800,7 @@
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@@ -9803,32 +10379,80 @@
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+      "language_name": "Swahili",
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       "bcp_47": "zh",
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