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  1. .gitignore +2 -0
  2. 50kspm_tokenizer.model +3 -0
  3. 50kspm_tokenizer.vocab +0 -0
  4. all.model +3 -0
  5. all.vocab +0 -0
  6. bkp_readdataset.py +31 -0
  7. en_code_32kspm_tokenizer.model +3 -0
  8. en_code_32kspm_tokenizer.vocab +0 -0
  9. extend_vocab.py +31 -0
  10. gemma.model +3 -0
  11. hi-indiccorp-bgpt-bpe-tokenizer1/special_tokens_map.json +15 -0
  12. hi-indiccorp-bgpt-bpe-tokenizer1/tokenizer.json +0 -0
  13. hi-indiccorp-bgpt-bpe-tokenizer1/tokenizer_config.json +54 -0
  14. hi-indiccorp-llama-bgpt-bpe-tokenizer1/special_tokens_map.json +16 -0
  15. hi-indiccorp-llama-bgpt-bpe-tokenizer1/tokenizer.json +0 -0
  16. hi-indiccorp-llama-bgpt-bpe-tokenizer1/tokenizer_config.json +2061 -0
  17. hi-indiccorp-nllb-bgpt-bpe-tokenizer1/special_tokens_map.json +255 -0
  18. hi-indiccorp-nllb-bgpt-bpe-tokenizer1/tokenizer.json +0 -0
  19. hi-indiccorp-nllb-bgpt-bpe-tokenizer1/tokenizer_config.json +1878 -0
  20. hi-indiccorp-vanilla-bgpt-bpe-tokenizer1/special_tokens_map.json +5 -0
  21. hi-indiccorp-vanilla-bgpt-bpe-tokenizer1/tokenizer.json +0 -0
  22. hi-indiccorp-vanilla-bgpt-bpe-tokenizer1/tokenizer_config.json +19 -0
  23. hi-indiccorp-vanilla-bgpt-bpe-tokenizer1/vocab.json +0 -0
  24. hi-sanghara-llama-bgpt-bpe-tokenizer1/special_tokens_map.json +21 -0
  25. hi-sanghara-llama-bgpt-bpe-tokenizer1/tokenizer.json +0 -0
  26. hi-sanghara-llama-bgpt-bpe-tokenizer1/tokenizer_config.json +2090 -0
  27. hi-sanghara-xlmr-bgpt-bpe-tokenizer1/special_tokens_map.json +21 -0
  28. hi-sanghara-xlmr-bgpt-bpe-tokenizer1/tokenizer.json +0 -0
  29. hi-sanghara-xlmr-bgpt-bpe-tokenizer1/tokenizer_config.json +2090 -0
  30. inference_fertility.py +49 -0
  31. llama.model +3 -0
  32. multiLingualFertility.py +84 -0
  33. openhathi.py +18 -0
  34. parameters.log +21 -0
  35. readdataset.py +31 -0
  36. spm_fertility.py +55 -0
  37. spm_training_en_code.py +58 -0
  38. spm_training_hi_ben_en_code.py +63 -0
  39. spm_training_ta_te_kan_ml.py +70 -0
  40. spmbpe_fertility.py +66 -0
  41. ta_te_kan_ml_50kspm_tokenizer.model +3 -0
  42. ta_te_kan_ml_50kspm_tokenizer.vocab +0 -0
  43. testtokenize.py +7 -0
  44. train_multilingual_tokenizer.py +102 -0
  45. train_sanghara_multilingual_tokenizer.py +109 -0
  46. train_sangra_multilingual_thaokenizer.py +130 -0
  47. train_spm.py +76 -0
  48. trained-tokenizer/convert_tokenizer.py +81 -0
  49. trained-tokenizer/dravid_64k/ta_te_kan_ml_50kspm_tokenizer.model +3 -0
  50. trained-tokenizer/dravid_64k/ta_te_kan_ml_50kspm_tokenizer.vocab +0 -0
.gitignore ADDED
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+ size 1297326
50kspm_tokenizer.vocab ADDED
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all.model ADDED
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+ version https://git-lfs.github.com/spec/v1
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all.vocab ADDED
The diff for this file is too large to render. See raw diff
 
bkp_readdataset.py ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from datasets import concatenate_datasets, load_dataset, load_from_disk
2
+ import argparse
3
+ from tokenizers import Tokenizer, decoders, models, pre_tokenizers, processors, trainers
4
+ from transformers import GPT2TokenizerFast, AutoTokenizer
5
+ from datasets import config
6
+ from datasets import DatasetDict, Dataset
7
+
8
+ code_dataset_go= load_dataset('code_x_glue_ct_code_to_text','go',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
9
+ code_dataset_java= load_dataset('code_x_glue_ct_code_to_text','java',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
10
+ code_dataset_javascript= load_dataset('code_x_glue_ct_code_to_text','javascript',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
11
+ code_dataset_php= load_dataset('code_x_glue_ct_code_to_text','php',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
12
+ code_dataset_python= load_dataset('code_x_glue_ct_code_to_text','python',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
13
+ code_dataset_ruby= load_dataset('code_x_glue_ct_code_to_text','ruby',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
14
+
15
+ indic_datasets_hi= load_dataset('ai4bharat/sangraha', data_dir="verified/hin", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['type'][:1000000]
16
+ indic_datasets_bn= load_dataset('ai4bharat/sangraha', data_dir="verified/ben", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['type'][:1000000]
17
+ wikipedia_en = load_dataset("wikipedia", "20220301.en", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text'][:1000000]
18
+
19
+
20
+ combined_train_set=code_dataset_go+code_dataset_java+code_dataset_javascript+code_dataset_php+code_dataset_python+code_dataset_ruby+indic_datasets_hi + indic_datasets_bn + wikipedia_en
21
+
22
+ data = {
23
+ "train":{"text": combined_train_set},
24
+ "validation": {"text": []},
25
+ "test": {"text": []},
26
+ }
27
+ # print(data)
28
+ custom_dataset = DatasetDict()
29
+ for split in data:
30
+ custom_dataset[split] = Dataset.from_dict(data[split])
31
+ print(custom_dataset)
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+ size 758185
en_code_32kspm_tokenizer.vocab ADDED
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extend_vocab.py ADDED
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+ import os
2
+ import sentencepiece as spm
3
+ from datasets import load_dataset
4
+
5
+ # Load the pre-trained SentencePiece model
6
+ sp = spm.SentencePieceProcessor()
7
+ sp.load('wiki_en.model')
8
+
9
+ # Load the Hindi dataset
10
+ dataset_hi = load_dataset('ai4bharat/samanantar', 'hi', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
11
+ test_hi = dataset_hi['tgt'][:1000] # Assuming you want to use the first 10000 sentences for extending the vocabulary
12
+
13
+ with open('test_hi.txt', 'w', encoding='utf-8') as f:
14
+ for sample in test_hi:
15
+ f.write(sample + '\n')
16
+ # Concatenate all sentences into a single string
17
+ additional_text = "\n".join(test_hi)
18
+
19
+ # Train the model further to extend the vocabulary
20
+ spm.SentencePieceTrainer.Train(
21
+ input="test_hi.txt", model_prefix='wiki_extended', vocab_size=3000
22
+ )
23
+
24
+ # Save the updated model with the extended vocabulary in the current working directory
25
+ sp = spm.SentencePieceProcessor()
26
+ save_path = 'wiki_extended.model'
27
+ sp.Load(save_path)
28
+
29
+ # Alternatively, you can specify a different directory with a shorter path
30
+ # save_path = '/short/path/to/wiki_extended.model'
31
+ # sp.save(save_path)
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hi-indiccorp-bgpt-bpe-tokenizer1/special_tokens_map.json ADDED
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+ "eos_token": "</s>",
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+ "mask_token": {
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hi-indiccorp-bgpt-bpe-tokenizer1/tokenizer.json ADDED
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hi-indiccorp-bgpt-bpe-tokenizer1/tokenizer_config.json ADDED
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+ "1": {
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+ }
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+ },
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+ "bos_token": "<s>",
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+ "clean_up_tokenization_spaces": true,
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+ "model_max_length": 512,
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+ "sep_token": "</s>",
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+ "tokenizer_class": "XLMRobertaTokenizer",
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+ "unk_token": "<unk>"
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+ }
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+ }
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+ }
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hi-indiccorp-nllb-bgpt-bpe-tokenizer1/special_tokens_map.json ADDED
@@ -0,0 +1,255 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "ace_Arab",
4
+ "ace_Latn",
5
+ "acm_Arab",
6
+ "acq_Arab",
7
+ "aeb_Arab",
8
+ "afr_Latn",
9
+ "ajp_Arab",
10
+ "aka_Latn",
11
+ "amh_Ethi",
12
+ "apc_Arab",
13
+ "arb_Arab",
14
+ "ars_Arab",
15
+ "ary_Arab",
16
+ "arz_Arab",
17
+ "asm_Beng",
18
+ "ast_Latn",
19
+ "awa_Deva",
20
+ "ayr_Latn",
21
+ "azb_Arab",
22
+ "azj_Latn",
23
+ "bak_Cyrl",
24
+ "bam_Latn",
25
+ "ban_Latn",
26
+ "bel_Cyrl",
27
+ "bem_Latn",
28
+ "ben_Beng",
29
+ "bho_Deva",
30
+ "bjn_Arab",
31
+ "bjn_Latn",
32
+ "bod_Tibt",
33
+ "bos_Latn",
34
+ "bug_Latn",
35
+ "bul_Cyrl",
36
+ "cat_Latn",
37
+ "ceb_Latn",
38
+ "ces_Latn",
39
+ "cjk_Latn",
40
+ "ckb_Arab",
41
+ "crh_Latn",
42
+ "cym_Latn",
43
+ "dan_Latn",
44
+ "deu_Latn",
45
+ "dik_Latn",
46
+ "dyu_Latn",
47
+ "dzo_Tibt",
48
+ "ell_Grek",
49
+ "eng_Latn",
50
+ "epo_Latn",
51
+ "est_Latn",
52
+ "eus_Latn",
53
+ "ewe_Latn",
54
+ "fao_Latn",
55
+ "pes_Arab",
56
+ "fij_Latn",
57
+ "fin_Latn",
58
+ "fon_Latn",
59
+ "fra_Latn",
60
+ "fur_Latn",
61
+ "fuv_Latn",
62
+ "gla_Latn",
63
+ "gle_Latn",
64
+ "glg_Latn",
65
+ "grn_Latn",
66
+ "guj_Gujr",
67
+ "hat_Latn",
68
+ "hau_Latn",
69
+ "heb_Hebr",
70
+ "hin_Deva",
71
+ "hne_Deva",
72
+ "hrv_Latn",
73
+ "hun_Latn",
74
+ "hye_Armn",
75
+ "ibo_Latn",
76
+ "ilo_Latn",
77
+ "ind_Latn",
78
+ "isl_Latn",
79
+ "ita_Latn",
80
+ "jav_Latn",
81
+ "jpn_Jpan",
82
+ "kab_Latn",
83
+ "kac_Latn",
84
+ "kam_Latn",
85
+ "kan_Knda",
86
+ "kas_Arab",
87
+ "kas_Deva",
88
+ "kat_Geor",
89
+ "knc_Arab",
90
+ "knc_Latn",
91
+ "kaz_Cyrl",
92
+ "kbp_Latn",
93
+ "kea_Latn",
94
+ "khm_Khmr",
95
+ "kik_Latn",
96
+ "kin_Latn",
97
+ "kir_Cyrl",
98
+ "kmb_Latn",
99
+ "kon_Latn",
100
+ "kor_Hang",
101
+ "kmr_Latn",
102
+ "lao_Laoo",
103
+ "lvs_Latn",
104
+ "lij_Latn",
105
+ "lim_Latn",
106
+ "lin_Latn",
107
+ "lit_Latn",
108
+ "lmo_Latn",
109
+ "ltg_Latn",
110
+ "ltz_Latn",
111
+ "lua_Latn",
112
+ "lug_Latn",
113
+ "luo_Latn",
114
+ "lus_Latn",
115
+ "mag_Deva",
116
+ "mai_Deva",
117
+ "mal_Mlym",
118
+ "mar_Deva",
119
+ "min_Latn",
120
+ "mkd_Cyrl",
121
+ "plt_Latn",
122
+ "mlt_Latn",
123
+ "mni_Beng",
124
+ "khk_Cyrl",
125
+ "mos_Latn",
126
+ "mri_Latn",
127
+ "zsm_Latn",
128
+ "mya_Mymr",
129
+ "nld_Latn",
130
+ "nno_Latn",
131
+ "nob_Latn",
132
+ "npi_Deva",
133
+ "nso_Latn",
134
+ "nus_Latn",
135
+ "nya_Latn",
136
+ "oci_Latn",
137
+ "gaz_Latn",
138
+ "ory_Orya",
139
+ "pag_Latn",
140
+ "pan_Guru",
141
+ "pap_Latn",
142
+ "pol_Latn",
143
+ "por_Latn",
144
+ "prs_Arab",
145
+ "pbt_Arab",
146
+ "quy_Latn",
147
+ "ron_Latn",
148
+ "run_Latn",
149
+ "rus_Cyrl",
150
+ "sag_Latn",
151
+ "san_Deva",
152
+ "sat_Beng",
153
+ "scn_Latn",
154
+ "shn_Mymr",
155
+ "sin_Sinh",
156
+ "slk_Latn",
157
+ "slv_Latn",
158
+ "smo_Latn",
159
+ "sna_Latn",
160
+ "snd_Arab",
161
+ "som_Latn",
162
+ "sot_Latn",
163
+ "spa_Latn",
164
+ "als_Latn",
165
+ "srd_Latn",
166
+ "srp_Cyrl",
167
+ "ssw_Latn",
168
+ "sun_Latn",
169
+ "swe_Latn",
170
+ "swh_Latn",
171
+ "szl_Latn",
172
+ "tam_Taml",
173
+ "tat_Cyrl",
174
+ "tel_Telu",
175
+ "tgk_Cyrl",
176
+ "tgl_Latn",
177
+ "tha_Thai",
178
+ "tir_Ethi",
179
+ "taq_Latn",
180
+ "taq_Tfng",
181
+ "tpi_Latn",
182
+ "tsn_Latn",
183
+ "tso_Latn",
184
+ "tuk_Latn",
185
+ "tum_Latn",
186
+ "tur_Latn",
187
+ "twi_Latn",
188
+ "tzm_Tfng",
189
+ "uig_Arab",
190
+ "ukr_Cyrl",
191
+ "umb_Latn",
192
+ "urd_Arab",
193
+ "uzn_Latn",
194
+ "vec_Latn",
195
+ "vie_Latn",
196
+ "war_Latn",
197
+ "wol_Latn",
198
+ "xho_Latn",
199
+ "ydd_Hebr",
200
+ "yor_Latn",
201
+ "yue_Hant",
202
+ "zho_Hans",
203
+ "zho_Hant",
204
+ "zul_Latn"
205
+ ],
206
+ "bos_token": {
207
+ "content": "<s>",
208
+ "lstrip": false,
209
+ "normalized": false,
210
+ "rstrip": false,
211
+ "single_word": false
212
+ },
213
+ "cls_token": {
214
+ "content": "<s>",
215
+ "lstrip": false,
216
+ "normalized": false,
217
+ "rstrip": false,
218
+ "single_word": false
219
+ },
220
+ "eos_token": {
221
+ "content": "</s>",
222
+ "lstrip": false,
223
+ "normalized": false,
224
+ "rstrip": false,
225
+ "single_word": false
226
+ },
227
+ "mask_token": {
228
+ "content": "<mask>",
229
+ "lstrip": true,
230
+ "normalized": true,
231
+ "rstrip": false,
232
+ "single_word": false
233
+ },
234
+ "pad_token": {
235
+ "content": "<pad>",
236
+ "lstrip": false,
237
+ "normalized": false,
238
+ "rstrip": false,
239
+ "single_word": false
240
+ },
241
+ "sep_token": {
242
+ "content": "</s>",
243
+ "lstrip": false,
244
+ "normalized": false,
245
+ "rstrip": false,
246
+ "single_word": false
247
+ },
248
+ "unk_token": {
249
+ "content": "<unk>",
250
+ "lstrip": false,
251
+ "normalized": false,
252
+ "rstrip": false,
253
+ "single_word": false
254
+ }
255
+ }
hi-indiccorp-nllb-bgpt-bpe-tokenizer1/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
hi-indiccorp-nllb-bgpt-bpe-tokenizer1/tokenizer_config.json ADDED
@@ -0,0 +1,1878 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "<s>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "1": {
12
+ "content": "<pad>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "</s>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "3": {
28
+ "content": "<unk>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "4": {
36
+ "content": "ace_Arab",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ },
43
+ "5": {
44
+ "content": "ace_Latn",
45
+ "lstrip": false,
46
+ "normalized": false,
47
+ "rstrip": false,
48
+ "single_word": false,
49
+ "special": true
50
+ },
51
+ "6": {
52
+ "content": "acm_Arab",
53
+ "lstrip": false,
54
+ "normalized": false,
55
+ "rstrip": false,
56
+ "single_word": false,
57
+ "special": true
58
+ },
59
+ "7": {
60
+ "content": "acq_Arab",
61
+ "lstrip": false,
62
+ "normalized": false,
63
+ "rstrip": false,
64
+ "single_word": false,
65
+ "special": true
66
+ },
67
+ "8": {
68
+ "content": "aeb_Arab",
69
+ "lstrip": false,
70
+ "normalized": false,
71
+ "rstrip": false,
72
+ "single_word": false,
73
+ "special": true
74
+ },
75
+ "9": {
76
+ "content": "afr_Latn",
77
+ "lstrip": false,
78
+ "normalized": false,
79
+ "rstrip": false,
80
+ "single_word": false,
81
+ "special": true
82
+ },
83
+ "10": {
84
+ "content": "ajp_Arab",
85
+ "lstrip": false,
86
+ "normalized": false,
87
+ "rstrip": false,
88
+ "single_word": false,
89
+ "special": true
90
+ },
91
+ "11": {
92
+ "content": "aka_Latn",
93
+ "lstrip": false,
94
+ "normalized": false,
95
+ "rstrip": false,
96
+ "single_word": false,
97
+ "special": true
98
+ },
99
+ "12": {
100
+ "content": "amh_Ethi",
101
+ "lstrip": false,
102
+ "normalized": false,
103
+ "rstrip": false,
104
+ "single_word": false,
105
+ "special": true
106
+ },
107
+ "13": {
108
+ "content": "apc_Arab",
109
+ "lstrip": false,
110
+ "normalized": false,
111
+ "rstrip": false,
112
+ "single_word": false,
113
+ "special": true
114
+ },
115
+ "14": {
116
+ "content": "arb_Arab",
117
+ "lstrip": false,
118
+ "normalized": false,
119
+ "rstrip": false,
120
+ "single_word": false,
121
+ "special": true
122
+ },
123
+ "15": {
124
+ "content": "ars_Arab",
125
+ "lstrip": false,
126
+ "normalized": false,
127
+ "rstrip": false,
128
+ "single_word": false,
129
+ "special": true
130
+ },
131
+ "16": {
132
+ "content": "ary_Arab",
133
+ "lstrip": false,
134
+ "normalized": false,
135
+ "rstrip": false,
136
+ "single_word": false,
137
+ "special": true
138
+ },
139
+ "17": {
140
+ "content": "arz_Arab",
141
+ "lstrip": false,
142
+ "normalized": false,
143
+ "rstrip": false,
144
+ "single_word": false,
145
+ "special": true
146
+ },
147
+ "18": {
148
+ "content": "asm_Beng",
149
+ "lstrip": false,
150
+ "normalized": false,
151
+ "rstrip": false,
152
+ "single_word": false,
153
+ "special": true
154
+ },
155
+ "19": {
156
+ "content": "ast_Latn",
157
+ "lstrip": false,
158
+ "normalized": false,
159
+ "rstrip": false,
160
+ "single_word": false,
161
+ "special": true
162
+ },
163
+ "20": {
164
+ "content": "awa_Deva",
165
+ "lstrip": false,
166
+ "normalized": false,
167
+ "rstrip": false,
168
+ "single_word": false,
169
+ "special": true
170
+ },
171
+ "21": {
172
+ "content": "ayr_Latn",
173
+ "lstrip": false,
174
+ "normalized": false,
175
+ "rstrip": false,
176
+ "single_word": false,
177
+ "special": true
178
+ },
179
+ "22": {
180
+ "content": "azb_Arab",
181
+ "lstrip": false,
182
+ "normalized": false,
183
+ "rstrip": false,
184
+ "single_word": false,
185
+ "special": true
186
+ },
187
+ "23": {
188
+ "content": "azj_Latn",
189
+ "lstrip": false,
190
+ "normalized": false,
191
+ "rstrip": false,
192
+ "single_word": false,
193
+ "special": true
194
+ },
195
+ "24": {
196
+ "content": "bak_Cyrl",
197
+ "lstrip": false,
198
+ "normalized": false,
199
+ "rstrip": false,
200
+ "single_word": false,
201
+ "special": true
202
+ },
203
+ "25": {
204
+ "content": "bam_Latn",
205
+ "lstrip": false,
206
+ "normalized": false,
207
+ "rstrip": false,
208
+ "single_word": false,
209
+ "special": true
210
+ },
211
+ "26": {
212
+ "content": "ban_Latn",
213
+ "lstrip": false,
214
+ "normalized": false,
215
+ "rstrip": false,
216
+ "single_word": false,
217
+ "special": true
218
+ },
219
+ "27": {
220
+ "content": "bel_Cyrl",
221
+ "lstrip": false,
222
+ "normalized": false,
223
+ "rstrip": false,
224
+ "single_word": false,
225
+ "special": true
226
+ },
227
+ "28": {
228
+ "content": "bem_Latn",
229
+ "lstrip": false,
230
+ "normalized": false,
231
+ "rstrip": false,
232
+ "single_word": false,
233
+ "special": true
234
+ },
235
+ "29": {
236
+ "content": "ben_Beng",
237
+ "lstrip": false,
238
+ "normalized": false,
239
+ "rstrip": false,
240
+ "single_word": false,
241
+ "special": true
242
+ },
243
+ "30": {
244
+ "content": "bho_Deva",
245
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hi-sanghara-xlmr-bgpt-bpe-tokenizer1/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
hi-sanghara-xlmr-bgpt-bpe-tokenizer1/tokenizer_config.json ADDED
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+ "additional_special_tokens": [
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+ "<unk>",
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+ "<pad>",
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+ "<mask>"
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+ ],
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+ "bos_token": "<|begin_of_text|>",
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+ "clean_up_tokenization_spaces": true,
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+ "eos_token": "<|end_of_text|>",
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+ "model_input_names": [
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+ "input_ids",
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+ "attention_mask"
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+ ],
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+ "tokenizer_class": "PreTrainedTokenizerFast"
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+ }
inference_fertility.py ADDED
@@ -0,0 +1,49 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from datasets import concatenate_datasets, load_dataset, load_from_disk
2
+ import argparse
3
+ from tokenizers import Tokenizer, decoders, models, pre_tokenizers, processors, trainers
4
+ from transformers import GPT2TokenizerFast, AutoTokenizer
5
+ from datasets import config
6
+ import logging
7
+ from datasets import DatasetDict, Dataset
8
+ import csv
9
+ import time
10
+ import json
11
+ tokenizer = AutoTokenizer.from_pretrained('Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Base')
12
+ def initialize_logger(log_file):
13
+ logging.basicConfig(filename=log_file, level=logging.INFO, format='%(asctime)s: %(message)s')
14
+
15
+ def log_parameters(vocab_size, pretrained_model, en_fertility_score, hi_fertility_score , ta_fertility_score , log_file='parameters.log'):
16
+ initialize_logger(log_file)
17
+ logging.info(f"Vocabulary Size: {vocab_size}, Tokenizer type: {pretrained_model}, English Fertility Score: {en_fertility_score} , Hindi Fertility Score: {hi_fertility_score}, Telugu Fertility Score: {ta_fertility_score}")
18
+
19
+ dataset_hi= load_dataset('ai4bharat/samanantar', 'hi', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
20
+ dataset_ta= load_dataset('ai4bharat/samanantar', 'te', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
21
+
22
+ test_en = dataset_hi['src'][:10000]
23
+ test_hi = dataset_hi['tgt'][:10000]
24
+ test_ta = dataset_ta['tgt'][:10000]
25
+
26
+ en_fertility_score=0
27
+ hi_fertility_score=0
28
+ ta_fertility_score=0
29
+
30
+ for data in test_en:
31
+ tok=tokenizer(data)
32
+ en_fertility_score += len(tok['input_ids']) / len(data.split())
33
+ en_fertility_score=en_fertility_score/10000
34
+
35
+ for data in test_hi:
36
+ # print(data)
37
+ tok=tokenizer(data)
38
+ # print(tok)
39
+ # exit()
40
+ hi_fertility_score += len(tok['input_ids']) / len(data.split())
41
+ hi_fertility_score=hi_fertility_score/10000
42
+
43
+ for data in test_ta:
44
+ tok=tokenizer(data)
45
+ ta_fertility_score += len(tok['input_ids']) / len(data.split())
46
+ ta_fertility_score=ta_fertility_score/10000
47
+ log_parameters(64000, "Telugu-Llama7B", en_fertility_score, hi_fertility_score , ta_fertility_score )
48
+
49
+
llama.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
3
+ size 499723
multiLingualFertility.py ADDED
@@ -0,0 +1,84 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import sentencepiece as spm
2
+ from datasets import concatenate_datasets, load_dataset, load_from_disk
3
+ import logging
4
+ from datasets import DatasetDict, Dataset
5
+
6
+ sp = spm.SentencePieceProcessor()
7
+ sp.load('ta_te_kan_ml_50kspm_tokenizer.model')
8
+ sentence = "The Indian cricket fans were very disappointed after India's loss against Australia in the World Cup."
9
+
10
+ tokens = sp.encode(sentence, out_type=str)
11
+ print(len(tokens) , tokens)
12
+
13
+ sentence = "विश्व कप में ऑस्ट्रेलिया के खिलाफ भारत की हार के बाद भारतीय क्रिकेट प्रशंसक काफी निराश थे।"
14
+
15
+ tokens = sp.encode(sentence, out_type=str)
16
+ print(len(tokens) , tokens)
17
+
18
+ decoded_sentence = sp.decode(tokens)
19
+
20
+ # exit()
21
+ # print("Tokens:", tokens)
22
+ # print("Decoded sentence:", decoded_sentence)
23
+ # exit()
24
+ def initialize_logger(log_file):
25
+ logging.basicConfig(filename=log_file, level=logging.INFO, format='%(asctime)s: %(message)s')
26
+
27
+ def log_parameters(vocab_size, pretrained_model, en_fertility_score, hi_fertility_score , kn_fertility_score ,ml_fertility_score,ta_fertility_score,te_fertility_score, log_file='parameters.log'):
28
+ initialize_logger(log_file)
29
+ logging.info(f"Vocabulary Size: {vocab_size}, Tokenizer type: {pretrained_model}, English Fertility Score: {en_fertility_score} , Hindi Fertility Score: {hi_fertility_score}, Kannada Fertility Score: {kn_fertility_score}, <Malayalam Fertility Score: {ml_fertility_score}, Tamil Fertility Score: {ta_fertility_score}, Telugu Fertility Score: {te_fertility_score}")
30
+
31
+ dataset_hi= load_dataset('ai4bharat/samanantar', 'hi', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
32
+ dataset_kn= load_dataset('ai4bharat/samanantar', 'kn', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
33
+ dataset_ml= load_dataset('ai4bharat/samanantar', 'ml', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
34
+ dataset_ta= load_dataset('ai4bharat/samanantar', 'ta', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
35
+ dataset_te= load_dataset('ai4bharat/samanantar', 'te', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
36
+
37
+ test_en = dataset_hi['src'][:10000]
38
+ test_hi = dataset_hi['tgt'][:10000]
39
+ test_kn = dataset_kn['tgt'][:10000]
40
+ test_ml = dataset_ml['tgt'][:10000]
41
+ test_ta = dataset_ta['tgt'][:10000]
42
+ test_te = dataset_te['tgt'][:10000]
43
+
44
+ en_fertility_score=0
45
+ hi_fertility_score=0
46
+ kn_fertility_score=0
47
+ ml_fertility_score=0
48
+ ta_fertility_score=0
49
+ te_fertility_score=0
50
+
51
+ for data in test_en:
52
+ tok=sp.encode(data, out_type=str)
53
+ en_fertility_score += (len(tok)) / len(data.split())
54
+ en_fertility_score=en_fertility_score/10000
55
+
56
+ for data in test_hi:
57
+ tok=sp.encode(data, out_type=str)
58
+ hi_fertility_score += (len(tok)) / len(data.split())
59
+ hi_fertility_score=hi_fertility_score/10000
60
+
61
+ for data in test_kn:
62
+ tok=sp.encode(data, out_type=str)
63
+ kn_fertility_score += (len(tok)) / len(data.split())
64
+ kn_fertility_score=kn_fertility_score/10000
65
+
66
+ for data in test_ml:
67
+ tok=sp.encode(data, out_type=str)
68
+ ml_fertility_score += (len(tok)) / len(data.split())
69
+ ml_fertility_score=ml_fertility_score/10000
70
+
71
+ for data in test_ta:
72
+ tok=sp.encode(data, out_type=str)
73
+ ta_fertility_score += (len(tok)) / len(data.split())
74
+ ta_fertility_score=ta_fertility_score/10000
75
+
76
+ for data in test_te:
77
+ tok=sp.encode(data, out_type=str)
78
+ te_fertility_score += (len(tok)) / len(data.split())
79
+ te_fertility_score=te_fertility_score/10000
80
+
81
+ log_parameters(64000, "kn-ml-ta-te-only-spm", en_fertility_score, hi_fertility_score , kn_fertility_score, ml_fertility_score,ta_fertility_score,te_fertility_score )
82
+
83
+
84
+
openhathi.py ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from transformers import LlamaTokenizer, LlamaForCausalLM
3
+
4
+ tokenizer = LlamaTokenizer.from_pretrained('sarvamai/OpenHathi-7B-Hi-v0.1-Base')
5
+ # model = LlamaForCausalLM.from_pretrained('sarvamai/OpenHathi-7B-Hi-v0.1-Base', torch_dtype=torch.bfloat16)
6
+
7
+ prompt = "The Indian cricket fans were very disappointed after India's loss against Australia in the World Cup."
8
+ inputs = tokenizer.encode(prompt)#, return_tensors="pt")
9
+ print(len(inputs))
10
+ print(tokenizer.decode(inputs))
11
+
12
+ prompt="विश्व कप में ऑस्ट्रेलिया के खिलाफ भारत की हार के बाद भारतीय क्रिकेट प्रशंसक काफी निराश थे।"
13
+ inputs = tokenizer.encode(prompt)#, return_tensors="pt")
14
+ print(len(inputs))
15
+ print(tokenizer.decode(inputs))
16
+ # Generate
17
+ # generate_ids = model.generate(inputs.input_ids, max_length=30)
18
+ # tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
parameters.log ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2024-05-10 14:05:26,938: Vocabulary Size: 128000, Tokenizer type: xlmr, English Fertility Score: 1.4939587001637604 , Hindi Fertility Score: 1.4488810128366754, Bengali Fertility Score: 1.685852085065919
2
+ 2024-05-10 14:56:23,136: Vocabulary Size: 128000, Tokenizer type: vanilla-hf, English Fertility Score: 1.216030679350266 , Hindi Fertility Score: 3.3398771973166177, Bengali Fertility Score: 4.365239037218936
3
+ 2024-05-10 20:00:26,548: Vocabulary Size: 128000, Tokenizer type: nllb, English Fertility Score: 1.3865334434853347 , Hindi Fertility Score: 1.3295224793229916, Bengali Fertility Score: 1.5777982910842563
4
+ 2024-05-11 09:03:36,243: Vocabulary Size: 128000, Tokenizer type: llama, English Fertility Score: 1.33709233640077 , Hindi Fertility Score: 2.6354341131028938, Bengali Fertility Score: 3.18294897828552
5
+ 2024-05-14 13:53:11,419: Vocabulary Size: 64000, Tokenizer type: sn-llama, English Fertility Score: 1.421999744923488 , Hindi Fertility Score: 6.548864060638438, Bengali Fertility Score: 10.249289334003336
6
+ 2024-05-14 14:06:10,326: Vocabulary Size: 10000, Tokenizer type: spm-10k, English Fertility Score: 1.9193645154760173 , Hindi Fertility Score: 1.78526953051212, Bengali Fertility Score: 2.111706690937606
7
+ 2024-05-14 14:58:28,856: Vocabulary Size: 256000, Tokenizer type: gemma, English Fertility Score: 1.2978079671801235 , Hindi Fertility Score: 2.069794706772813, Bengali Fertility Score: 3.680755926459185
8
+ 2024-05-14 15:00:29,557: Vocabulary Size: 32000, Tokenizer type: llama, English Fertility Score: 1.5202502831938836 , Hindi Fertility Score: 5.589527756146963, Bengali Fertility Score: 8.23128918670597
9
+ 2024-05-14 15:03:31,775: Vocabulary Size: 50304, Tokenizer type: spm, English Fertility Score: 1.431964737612782 , Hindi Fertility Score: 1.3786534143723854, Bengali Fertility Score: 1.5750431160348508
10
+ 2024-05-14 16:16:59,436: Vocabulary Size: 64000, Tokenizer type: sn-llama, English Fertility Score: 1.421999744923488 , Hindi Fertility Score: 6.548864060638438, Bengali Fertility Score: 10.249289334003336
11
+ 2024-05-14 17:09:58,592: Vocabulary Size: 64000, Tokenizer type: spm-64k, English Fertility Score: 1.3808169451199286 , Hindi Fertility Score: 1.348962422639297, Bengali Fertility Score: 1.5328664693969913
12
+ 2024-05-15 04:15:03,431: Vocabulary Size: 64000, Tokenizer type: spm1-64k-more-data, English Fertility Score: 1.3816591628453216 , Hindi Fertility Score: 1.3514455428519547, Bengali Fertility Score: 1.537261719216952
13
+ 2024-05-16 05:18:07,554: Vocabulary Size: 50304, Tokenizer type: spm-50k-code, English Fertility Score: 1.473119583234624 , Hindi Fertility Score: 1.4287223769079143, Bengali Fertility Score: 1.5827995876834988
14
+ 2024-05-16 05:22:15,039: Vocabulary Size: 256000, Tokenizer type: gemma, English Fertility Score: 1.5202502831938836 , Hindi Fertility Score: 5.589527756146963, Bengali Fertility Score: 8.23128918670597
15
+ 2024-05-16 07:31:28,401: Vocabulary Size: 64000, Tokenizer type: OpenHathi, English Fertility Score: 1.6226753699588483 , Hindi Fertility Score: 1.9203947938483794, Bengali Fertility Score: 8.397392248172588
16
+ 2024-05-16 07:36:20,565: Vocabulary Size: 64000, Tokenizer type: BanglaLLM-Llama7B, English Fertility Score: 1.5987634409147589 , Hindi Fertility Score: 5.39511427337356, Bengali Fertility Score: 2.8090354815260925
17
+ 2024-05-16 08:14:12,384: Vocabulary Size: 32000, Tokenizer type: en-only-spm, English Fertility Score: 1.4289468009158557 , Hindi Fertility Score: 5.970752059626246, Bengali Fertility Score: 10.555846774647051
18
+ 2024-05-16 13:00:27,914: Vocabulary Size: 64000, Tokenizer type: kn-ml-ta-te-only-spm, English Fertility Score: 1.5202244953745054 , Hindi Fertility Score: 1.4787130913799276, Kannada Fertility Score: 2.0937033059503114, Malayalam Fertility Score: 2.574488091810092, Tamil Fertility Score: 2.0626169250890873, Telugu Fertility Score: 2.0837862630934447
19
+ 2024-05-16 13:17:20,889: Vocabulary Size: 64000, Tokenizer type: malayalam-Llama7B, English Fertility Score: 1.7270884382070268 , Hindi Fertility Score: 5.387804477509378, Malayalam Fertility Score: 2.473338499094062
20
+ 2024-05-16 13:28:38,920: Vocabulary Size: 64000, Tokenizer type: Tamil-Llama7B, English Fertility Score: 1.5757223407045382 , Hindi Fertility Score: 5.639966225244974, Tamil Fertility Score: 2.469817051159225
21
+ 2024-05-16 14:41:45,630: Vocabulary Size: 64000, Tokenizer type: Telugu-Llama7B, English Fertility Score: 1.6226753699588483 , Hindi Fertility Score: 5.680307846730889, Telugu Fertility Score: 20.23636042989792
readdataset.py ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from datasets import concatenate_datasets, load_dataset, load_from_disk
2
+ import argparse
3
+ from tokenizers import Tokenizer, decoders, models, pre_tokenizers, processors, trainers
4
+ from transformers import GPT2TokenizerFast, AutoTokenizer
5
+ from datasets import config
6
+ from datasets import DatasetDict, Dataset
7
+
8
+ code_dataset_go= load_dataset('code_x_glue_ct_code_to_text','go',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
9
+ code_dataset_java= load_dataset('code_x_glue_ct_code_to_text','java',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
10
+ code_dataset_javascript= load_dataset('code_x_glue_ct_code_to_text','javascript',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
11
+ code_dataset_php= load_dataset('code_x_glue_ct_code_to_text','php',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
12
+ code_dataset_python= load_dataset('code_x_glue_ct_code_to_text','python',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
13
+ code_dataset_ruby= load_dataset('code_x_glue_ct_code_to_text','ruby',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
14
+
15
+ indic_datasets_hi= load_dataset('ai4bharat/sangraha', data_dir="verified/hin", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['type'][:1000000]
16
+ indic_datasets_bn= load_dataset('ai4bharat/sangraha', data_dir="verified/ben", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['type'][:1000000]
17
+ wikipedia_en = load_dataset("wikipedia", "20220301.en", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text'][:1000000]
18
+
19
+
20
+ combined_train_set=code_dataset_go+code_dataset_java+code_dataset_javascript+code_dataset_php+code_dataset_python+code_dataset_ruby+indic_datasets_hi + indic_datasets_bn + wikipedia_en
21
+
22
+ data = {
23
+ "train":{"text": combined_train_set},
24
+ "validation": {"text": []},
25
+ "test": {"text": []},
26
+ }
27
+ # print(data)
28
+ custom_dataset = DatasetDict()
29
+ for split in data:
30
+ custom_dataset[split] = Dataset.from_dict(data[split])
31
+ print(custom_dataset)
spm_fertility.py ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import sentencepiece as spm
2
+ from datasets import load_dataset
3
+
4
+ def load_model(model_path):
5
+ """ Load the SentencePiece model from the specified path. """
6
+ sp = spm.SentencePieceProcessor()
7
+ sp.Load(model_path)
8
+ return sp
9
+
10
+ def tokenize_text(sp, text):
11
+ """ Tokenize a list of sentences and return the tokens. """
12
+ tokenized_text = [sp.EncodeAsPieces(sentence) for sentence in text]
13
+ return tokenized_text
14
+
15
+ def calculate_fertility(tokenized_text):
16
+ """ Calculate the average fertility of the tokenized text. """
17
+ total_words = sum(len(sentence.split()) for sentence in tokenized_text)
18
+ total_tokens = sum(len(tokens) for tokens in tokenize_text(sp, tokenized_text))
19
+ return total_tokens / total_words if total_words > 0 else 0
20
+
21
+ # Path to your SentencePiece model
22
+ model_path = 'gemma.model'
23
+ sp = load_model(model_path)
24
+
25
+ dataset_hi= load_dataset('ai4bharat/samanantar', 'hi', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
26
+ dataset_bn= load_dataset('ai4bharat/samanantar', 'bn', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
27
+
28
+ # indic_datasets_hi= load_dataset('ai4bharat/sangraha', data_dir="verified/hin", cache_dir='/sml2/atul/CENTRAL_CACHE')['test']['text']
29
+ # indic_datasets_bn= load_dataset('ai4bharat/sangraha', data_dir="verified/ben", cache_dir='/sml2/atul/CENTRAL_CACHE')['test']['text']
30
+ # wikipedia_en = load_dataset("wikipedia", "20220301.en", cache_dir='/sml2/atul/CENTRAL_CACHE')['test']['text']
31
+ # Tokenize the sentences
32
+
33
+ sentences = dataset_hi['tgt'][:10000]
34
+ tokenized_sentences = tokenize_text(sp, sentences)
35
+ # Calculate the fertility
36
+ fertility = calculate_fertility(sentences)
37
+
38
+ print("Fertility of Hindi tokenizer:", fertility)
39
+
40
+
41
+ sentences = dataset_bn['tgt'][:10000]
42
+ tokenized_sentences = tokenize_text(sp, sentences)
43
+ # Calculate the fertility
44
+ fertility = calculate_fertility(sentences)
45
+
46
+ print("Fertility of Bengali tokenizer:", fertility)
47
+
48
+
49
+ sentences = dataset_hi['src'][:10000]
50
+ tokenized_sentences = tokenize_text(sp, sentences)
51
+ # Calculate the fertility
52
+ fertility = calculate_fertility(sentences)
53
+
54
+ print("Fertility of English tokenizer:", fertility)
55
+
spm_training_en_code.py ADDED
@@ -0,0 +1,58 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import sentencepiece as spm
2
+ import re
3
+
4
+
5
+ from datasets import load_dataset, DatasetDict, Dataset
6
+ import random
7
+
8
+ # # Load code datasets
9
+ code_dataset_go = load_dataset('code_x_glue_ct_code_to_text', 'go', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
10
+ code_dataset_java = load_dataset('code_x_glue_ct_code_to_text', 'java', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
11
+ code_dataset_javascript = load_dataset('code_x_glue_ct_code_to_text', 'javascript', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
12
+ code_dataset_php = load_dataset('code_x_glue_ct_code_to_text', 'php', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
13
+ code_dataset_python = load_dataset('code_x_glue_ct_code_to_text', 'python', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
14
+ code_dataset_ruby = load_dataset('code_x_glue_ct_code_to_text', 'ruby', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
15
+
16
+ wikipedia_en = load_dataset("wikipedia", "20220301.en", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
17
+
18
+ # # Randomly sample a subset of text data
19
+ num_docs = 1000000
20
+ en_sampled = random.sample(wikipedia_en, num_docs)
21
+
22
+ # # Combine sampled text datasets with code datasets
23
+ combined_train_set = en_sampled
24
+ combined_train_set_code = code_dataset_go + code_dataset_java + code_dataset_javascript + code_dataset_php + code_dataset_python + code_dataset_ruby
25
+
26
+ # # Write the combined data to the output file
27
+ with open('en_code_traintext.txt', 'w') as f:
28
+ for text in combined_train_set:
29
+ lines = text.split("\n")
30
+ for l in lines:
31
+ if l:
32
+ f.write(l.strip() + '\n')
33
+ for code in combined_train_set_code:
34
+ f.write(code + '\n')
35
+
36
+
37
+ spm.SentencePieceTrainer.Train(
38
+ input='en_code_traintext.txt',
39
+ model_prefix='en_code_50kspm_tokenizer',
40
+ vocab_size=32000,
41
+ pad_id=0,
42
+ unk_id=1,
43
+ bos_id=2,
44
+ eos_id=3,
45
+ pad_piece='<pad>',
46
+ unk_piece='<unk>',
47
+ bos_piece='<bos>',
48
+ eos_piece='<eos>',
49
+ model_type='bpe',
50
+ num_threads=256,
51
+ add_dummy_prefix=False,
52
+ byte_fallback=True,
53
+ character_coverage=0.9999,
54
+ remove_extra_whitespaces=False,
55
+ allow_whitespace_only_pieces=True,
56
+ split_digits=True,
57
+ user_defined_symbols='\n,\r,<pad>,<eos>,<bos>,<mask>,<unused0>,<unused1>,<unused2>,<unused3>,<unused4>,<unused5>,<unused6>,<unused7>,<unused8>,<unused9>,<unused10>,<unused11>,<unused12>,<unused13>,<unused14>,<unused15>,<unused16>,<unused17>,<unused18>,<unused19>,<unused20>,<unused21>,<unused22>,<unused23>,<unused24>,<unused25>,<unused26>,<unused27>,<unused28>,<unused29>,<unused30>,<unused31>,<unused32>,<unused33>,<unused34>,<unused35>,<unused36>,<unused37>,<unused38>,<unused39>,<unused40>,<unused41>,<unused42>,<unused43>,<unused44>,<unused45>,<unused46>,<unused47>,<unused48>,<unused49>,<unused50>,<unused51>,<unused52>,<unused53>,<unused54>,<unused55>,<unused56>,<unused57>,<unused58>,<unused59>,<unused60>,<unused61>,<unused62>,<unused63>,<unused64>,<unused65>,<unused66>,<unused67>,<unused68>,<unused69>,<unused70>,<unused71>,<unused72>,<unused73>,<unused74>,<unused75>,<unused76>,<unused77>,<unused78>,<unused79>,<unused80>,<unused81>,<unused82>,<unused83>,<unused84>,<unused85>,<unused86>,<unused87>,<unused88>,<unused89>,<unused90>,<unused91>,<unused92>,<unused93>,<unused94>,<unused95>,<unused96>,<unused97>,<unused98>,<start_of_turn>,<end_of_turn>,〈|javascript|〉,〈|python|〉,〈|sql|〉,〈|shell|〉,〈|c|〉,〈|cpp|〉,〈|java|〉,〈|go|〉',
58
+ )
spm_training_hi_ben_en_code.py ADDED
@@ -0,0 +1,63 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import sentencepiece as spm
2
+ import re
3
+
4
+
5
+ # from datasets import load_dataset, DatasetDict, Dataset
6
+ # import random
7
+
8
+ # # Load code datasets
9
+ # code_dataset_go = load_dataset('code_x_glue_ct_code_to_text', 'go', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
10
+ # code_dataset_java = load_dataset('code_x_glue_ct_code_to_text', 'java', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
11
+ # code_dataset_javascript = load_dataset('code_x_glue_ct_code_to_text', 'javascript', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
12
+ # code_dataset_php = load_dataset('code_x_glue_ct_code_to_text', 'php', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
13
+ # code_dataset_python = load_dataset('code_x_glue_ct_code_to_text', 'python', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
14
+ # code_dataset_ruby = load_dataset('code_x_glue_ct_code_to_text', 'ruby', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
15
+
16
+ # # Load text datasets
17
+ # indic_datasets_hi = load_dataset('ai4bharat/sangraha', data_dir="verified/hin", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
18
+ # indic_datasets_bn = load_dataset('ai4bharat/sangraha', data_dir="verified/ben", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
19
+ # wikipedia_en = load_dataset("wikipedia", "20220301.en", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
20
+
21
+ # # Randomly sample a subset of text data
22
+ # num_docs = 500000
23
+ # hi_sampled = random.sample(indic_datasets_hi, num_docs)
24
+ # bn_sampled = random.sample(indic_datasets_bn, num_docs)
25
+ # en_sampled = random.sample(wikipedia_en, num_docs)
26
+
27
+ # # Combine sampled text datasets with code datasets
28
+ # combined_train_set = en_sampled + hi_sampled + bn_sampled
29
+ # combined_train_set_code = code_dataset_go + code_dataset_java + code_dataset_javascript + code_dataset_php + code_dataset_python + code_dataset_ruby
30
+
31
+ # # Write the combined data to the output file
32
+ # with open('traintext1.txt', 'w') as f:
33
+ # for text in combined_train_set:
34
+ # lines = text.split("\n")
35
+ # for l in lines:
36
+ # if l:
37
+ # f.write(l.strip() + '\n')
38
+ # for code in combined_train_set_code:
39
+ # f.write(code + '\n')
40
+
41
+
42
+ spm.SentencePieceTrainer.Train(
43
+ input='traintext1.txt',
44
+ model_prefix='50kspm_tokenizer_code',
45
+ vocab_size=50304,
46
+ pad_id=0,
47
+ unk_id=1,
48
+ bos_id=2,
49
+ eos_id=3,
50
+ pad_piece='<pad>',
51
+ unk_piece='<unk>',
52
+ bos_piece='<bos>',
53
+ eos_piece='<eos>',
54
+ model_type='bpe',
55
+ num_threads=256,
56
+ add_dummy_prefix=False,
57
+ byte_fallback=True,
58
+ character_coverage=0.9999,
59
+ remove_extra_whitespaces=False,
60
+ allow_whitespace_only_pieces=True,
61
+ split_digits=True,
62
+ user_defined_symbols='\n,\r,<pad>,<eos>,<bos>,<mask>,<unused0>,<unused1>,<unused2>,<unused3>,<unused4>,<unused5>,<unused6>,<unused7>,<unused8>,<unused9>,<unused10>,<unused11>,<unused12>,<unused13>,<unused14>,<unused15>,<unused16>,<unused17>,<unused18>,<unused19>,<unused20>,<unused21>,<unused22>,<unused23>,<unused24>,<unused25>,<unused26>,<unused27>,<unused28>,<unused29>,<unused30>,<unused31>,<unused32>,<unused33>,<unused34>,<unused35>,<unused36>,<unused37>,<unused38>,<unused39>,<unused40>,<unused41>,<unused42>,<unused43>,<unused44>,<unused45>,<unused46>,<unused47>,<unused48>,<unused49>,<unused50>,<unused51>,<unused52>,<unused53>,<unused54>,<unused55>,<unused56>,<unused57>,<unused58>,<unused59>,<unused60>,<unused61>,<unused62>,<unused63>,<unused64>,<unused65>,<unused66>,<unused67>,<unused68>,<unused69>,<unused70>,<unused71>,<unused72>,<unused73>,<unused74>,<unused75>,<unused76>,<unused77>,<unused78>,<unused79>,<unused80>,<unused81>,<unused82>,<unused83>,<unused84>,<unused85>,<unused86>,<unused87>,<unused88>,<unused89>,<unused90>,<unused91>,<unused92>,<unused93>,<unused94>,<unused95>,<unused96>,<unused97>,<unused98>,<start_of_turn>,<end_of_turn>,〈|javascript|〉,〈|python|〉,〈|sql|〉,〈|shell|〉,〈|c|〉,〈|cpp|〉,〈|java|〉,〈|go|〉',
63
+ )
spm_training_ta_te_kan_ml.py ADDED
@@ -0,0 +1,70 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import sentencepiece as spm
2
+ import re
3
+
4
+
5
+ from datasets import load_dataset, DatasetDict, Dataset
6
+ import random
7
+
8
+ # Load code datasets
9
+ code_dataset_go = load_dataset('code_x_glue_ct_code_to_text', 'go', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
10
+ code_dataset_java = load_dataset('code_x_glue_ct_code_to_text', 'java', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
11
+ code_dataset_javascript = load_dataset('code_x_glue_ct_code_to_text', 'javascript', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
12
+ code_dataset_php = load_dataset('code_x_glue_ct_code_to_text', 'php', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
13
+ code_dataset_python = load_dataset('code_x_glue_ct_code_to_text', 'python', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
14
+ code_dataset_ruby = load_dataset('code_x_glue_ct_code_to_text', 'ruby', split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'][:400000]
15
+
16
+ # # Load text datasets
17
+ indic_datasets_hi = load_dataset('ai4bharat/sangraha', data_dir="verified/hin", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
18
+ indic_datasets_kan = load_dataset('ai4bharat/sangraha', data_dir="verified/kan", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
19
+ indic_datasets_ml = load_dataset('ai4bharat/sangraha', data_dir="verified/mal", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
20
+ indic_datasets_ta = load_dataset('ai4bharat/sangraha', data_dir="verified/tam", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
21
+ indic_datasets_te = load_dataset('ai4bharat/sangraha', data_dir="verified/tel", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
22
+
23
+ wikipedia_en = load_dataset("wikipedia", "20220301.en", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
24
+
25
+ # # Randomly sample a subset of text data
26
+ num_docs = 300000
27
+ hi_sampled = random.sample(indic_datasets_hi, num_docs)
28
+ kan_sampled = random.sample(indic_datasets_kan, num_docs)
29
+ ml_sampled = random.sample(indic_datasets_ml, num_docs)
30
+ ta_sampled = random.sample(indic_datasets_ta, num_docs)
31
+ te_sampled = random.sample(indic_datasets_te, num_docs)
32
+ en_sampled = random.sample(wikipedia_en, num_docs)
33
+
34
+ # # Combine sampled text datasets with code datasets
35
+ combined_train_set = en_sampled + hi_sampled + kan_sampled + ml_sampled + ta_sampled + te_sampled
36
+ combined_train_set_code = code_dataset_go + code_dataset_java + code_dataset_javascript + code_dataset_php + code_dataset_python + code_dataset_ruby
37
+
38
+ # # Write the combined data to the output file
39
+ with open('ta_te_kan_ml_traintext.txt', 'w') as f:
40
+ for text in combined_train_set:
41
+ lines = text.split("\n")
42
+ for l in lines:
43
+ if l:
44
+ f.write(l.strip() + '\n')
45
+ for code in combined_train_set_code:
46
+ f.write(code + '\n')
47
+
48
+
49
+ spm.SentencePieceTrainer.Train(
50
+ input='ta_te_kan_ml_traintext.txt',
51
+ model_prefix='ta_te_kan_ml_50kspm_tokenizer',
52
+ vocab_size=64000,
53
+ pad_id=0,
54
+ unk_id=1,
55
+ bos_id=2,
56
+ eos_id=3,
57
+ pad_piece='<pad>',
58
+ unk_piece='<unk>',
59
+ bos_piece='<bos>',
60
+ eos_piece='<eos>',
61
+ model_type='bpe',
62
+ num_threads=256,
63
+ add_dummy_prefix=False,
64
+ byte_fallback=True,
65
+ character_coverage=0.9999,
66
+ remove_extra_whitespaces=False,
67
+ allow_whitespace_only_pieces=True,
68
+ split_digits=True,
69
+ user_defined_symbols='\n,\r,<pad>,<eos>,<bos>,<mask>,<unused0>,<unused1>,<unused2>,<unused3>,<unused4>,<unused5>,<unused6>,<unused7>,<unused8>,<unused9>,<unused10>,<unused11>,<unused12>,<unused13>,<unused14>,<unused15>,<unused16>,<unused17>,<unused18>,<unused19>,<unused20>,<unused21>,<unused22>,<unused23>,<unused24>,<unused25>,<unused26>,<unused27>,<unused28>,<unused29>,<unused30>,<unused31>,<unused32>,<unused33>,<unused34>,<unused35>,<unused36>,<unused37>,<unused38>,<unused39>,<unused40>,<unused41>,<unused42>,<unused43>,<unused44>,<unused45>,<unused46>,<unused47>,<unused48>,<unused49>,<unused50>,<unused51>,<unused52>,<unused53>,<unused54>,<unused55>,<unused56>,<unused57>,<unused58>,<unused59>,<unused60>,<unused61>,<unused62>,<unused63>,<unused64>,<unused65>,<unused66>,<unused67>,<unused68>,<unused69>,<unused70>,<unused71>,<unused72>,<unused73>,<unused74>,<unused75>,<unused76>,<unused77>,<unused78>,<unused79>,<unused80>,<unused81>,<unused82>,<unused83>,<unused84>,<unused85>,<unused86>,<unused87>,<unused88>,<unused89>,<unused90>,<unused91>,<unused92>,<unused93>,<unused94>,<unused95>,<unused96>,<unused97>,<unused98>,<start_of_turn>,<end_of_turn>,〈|javascript|〉,〈|python|〉,〈|sql|〉,〈|shell|〉,〈|c|〉,〈|cpp|〉,〈|java|〉,〈|go|〉',
70
+ )
spmbpe_fertility.py ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import sentencepiece as spm
2
+ from datasets import concatenate_datasets, load_dataset, load_from_disk
3
+ import logging
4
+ from datasets import DatasetDict, Dataset
5
+
6
+ sp = spm.SentencePieceProcessor()
7
+ sp.load('ta_te_kan_ml_50kspm_tokenizer.model')
8
+ sentence = "ಮಾಡು ಮಾಡುವುದು ಮಾಡಲು विद्यालय आलय"
9
+ # ಮಾ', 'ಡುವುದು', '▁ಮಾಡಲು', '▁']
10
+ # 9 ['ಕುಳ', 'ಿತು', 'ಕೊಳ್ಳಿ', '▁▁', 'ಕುಳ', 'ಿತು', 'ಕೊ', 'ಳ್', 'ಲು']
11
+ # 'ഇ', 'ര', 'ിക്കും', '▁ഇരിക്ക', 'ുക', '▁'
12
+ # ['विश्व', '▁कप', '▁में', '▁काल', '▁अंश', '▁काला', 'ंश', '▁ऑस्ट्रेलिया', '▁के', '▁खिलाफ', '▁भारत', '▁की', '▁हार', '▁के', '▁बाद', '▁भारतीय', '▁क्रिकेट', '▁प्रशंसा', '▁काफी', '▁निराश', '▁थे', '।']
13
+ # विश्व', '▁कप', '▁में', '▁ऑस्ट्रेलिया', '▁के', '▁खिलाफ', '▁भारत', '▁की', '▁हार', '▁के', '▁बाद', '▁भारतीय', '▁क्रिकेट', '▁प्रशंसा', '▁काफी', '▁निराश', '▁थे', '।'
14
+ # 19 ['विश्व', '▁कप', '▁में', '▁ऑस्ट्रेलिया', '▁के', '▁खिलाफ', '▁भारत', '▁की', '▁हार', '▁के', '▁बाद', '▁भारतीय', '▁क्रिकेट', '▁प्रश', 'ंसक', '▁काफी', '▁निराश', '▁थे', '।']
15
+ # काला', 'ंश'
16
+ # 19 ['ലോക', 'ക', 'പ്പിൽ', '▁ഓസ്', '▁ട്ര', 'േലിയ', 'യ്', '▁', 'ക്കെതിരെ', '▁ഇന്ത്യ', '▁തോറ്റ', 'തോടെ', '▁ഇന്ത്യൻ', '▁ക്രിക്കറ്റ്', '▁ആരാധകർ', '▁ഏറെ', '▁നിരാശ', 'യിലാണ്', '.']
17
+ tokens = sp.encode(sentence, out_type=str)
18
+ print(len(tokens) , tokens)
19
+ exit()
20
+ sentence = "विश्व कप में ऑस्ट्रेलिया के खिलाफ भारत की हार के बाद भारतीय क्रिकेट प्रशंसक काफी निराश थे।"
21
+
22
+ tokens = sp.encode(sentence, out_type=str)
23
+ print(len(tokens) , tokens)
24
+
25
+ decoded_sentence = sp.decode(tokens)
26
+ exit()
27
+ # exit()
28
+ # print("Tokens:", tokens)
29
+ # print("Decoded sentence:", decoded_sentence)
30
+ # exit()
31
+ def initialize_logger(log_file):
32
+ logging.basicConfig(filename=log_file, level=logging.INFO, format='%(asctime)s: %(message)s')
33
+
34
+ def log_parameters(vocab_size, pretrained_model, en_fertility_score, hi_fertility_score , bn_fertility_score , log_file='parameters.log'):
35
+ initialize_logger(log_file)
36
+ logging.info(f"Vocabulary Size: {vocab_size}, Tokenizer type: {pretrained_model}, English Fertility Score: {en_fertility_score} , Hindi Fertility Score: {hi_fertility_score}, Bengali Fertility Score: {bn_fertility_score}")
37
+
38
+ dataset_hi= load_dataset('ai4bharat/samanantar', 'hi', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
39
+ dataset_bn= load_dataset('ai4bharat/samanantar', 'bn', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
40
+
41
+ test_en = dataset_hi['src'][:10000]
42
+ test_hi = dataset_hi['tgt'][:10000]
43
+ test_bn = dataset_bn['tgt'][:10000]
44
+
45
+ en_fertility_score=0
46
+ hi_fertility_score=0
47
+ bn_fertility_score=0
48
+
49
+ for data in test_en:
50
+ tok=sp.encode(data, out_type=str)
51
+ en_fertility_score += (len(tok)) / len(data.split())
52
+ en_fertility_score=en_fertility_score/10000
53
+
54
+ for data in test_hi:
55
+ tok=sp.encode(data, out_type=str)
56
+ hi_fertility_score += (len(tok)) / len(data.split())
57
+ hi_fertility_score=hi_fertility_score/10000
58
+
59
+ for data in test_bn:
60
+ tok=sp.encode(data, out_type=str)
61
+ bn_fertility_score += (len(tok)) / len(data.split())
62
+ bn_fertility_score=bn_fertility_score/10000
63
+ log_parameters(32000, "en-only-spm", en_fertility_score, hi_fertility_score , bn_fertility_score )
64
+
65
+
66
+
ta_te_kan_ml_50kspm_tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:00973ed5449fdba701c90de28f03d4ca5d63fd2111cfcc2396fdc99180685014
3
+ size 1719743
ta_te_kan_ml_50kspm_tokenizer.vocab ADDED
The diff for this file is too large to render. See raw diff
 
testtokenize.py ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ from tokenizers import Tokenizer, decoders, models, pre_tokenizers, processors, trainers
2
+ from transformers import AutoTokenizer
3
+
4
+ tokenizer = AutoTokenizer.from_pretrained('Tensoic/Kan-LLaMA-7B-base')
5
+ print(tokenizer)
6
+ st="ಮಾಡುವುದು ಮಾಡಲು"
7
+ print(tokenizer.convert_ids_to_tokens(tokenizer(st)['input_ids']))
train_multilingual_tokenizer.py ADDED
@@ -0,0 +1,102 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from datasets import concatenate_datasets, load_dataset, load_from_disk
2
+ import argparse
3
+ from tokenizers import Tokenizer, decoders, models, pre_tokenizers, processors, trainers
4
+ from transformers import GPT2TokenizerFast, AutoTokenizer
5
+ from datasets import config
6
+ from datasets import DatasetDict, Dataset
7
+ import logging
8
+
9
+ def initialize_logger(log_file):
10
+ logging.basicConfig(filename=log_file, level=logging.INFO, format='%(asctime)s: %(message)s')
11
+
12
+ def log_parameters(vocab_size, batch_size, fertility_score, proportion_continued_words, log_file='parameters.log'):
13
+ initialize_logger(log_file)
14
+ logging.info(f"Vocabulary Size: {vocab_size}, Batch Size: {batch_size}, Fertility Score: {fertility_score}, Proportion of Continued word: {proportion_continued_words}")
15
+
16
+ def parse_arguments():
17
+ parser = argparse.ArgumentParser()
18
+ parser.add_argument(
19
+ "--batch_size",
20
+ type=int,
21
+ required=True,
22
+ help="Batch size to use for training"
23
+ )
24
+ parser.add_argument(
25
+ "--vocab_size",
26
+ type=int,
27
+ required=True,
28
+ help="Vocabulary size to use for tokenizer"
29
+ )
30
+ parser.add_argument(
31
+ "--use_config",
32
+ choices=['xlm-roberta', 'vanilla','gemma'],
33
+ required=True,
34
+ help="Use XLM-RoBERTa config or Vanilla BPE"
35
+ )
36
+ parser.add_argument(
37
+ "--do_evaluate",
38
+ action='store_true',
39
+ help="Enable evaluation."
40
+ )
41
+ args = parser.parse_known_args()
42
+ return args
43
+ def train_tokenizer(args):
44
+ # indic_datasets_hi= load_dataset('satpalsr/indicCorpv2', 'hi', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')['text'][:20502390]
45
+ indic_datasets_en= load_dataset('satpalsr/indicCorpv2', 'en', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')['text'][:205090]
46
+ # indic_datasets_bn= load_dataset('satpalsr/indicCorpv2', 'bn', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')['text'][:20502390]
47
+ # combined_train_set= indic_datasets_hi + indic_datasets_en + indic_datasets_bn
48
+ combined_train_set=indic_datasets_en
49
+ data = {
50
+ "train":{"text": combined_train_set},
51
+ "validation": {"text": []},
52
+ "test": {"text": []},
53
+ }
54
+ # print(data)
55
+ custom_dataset = DatasetDict()
56
+ for split in data:
57
+ custom_dataset[split] = Dataset.from_dict(data[split])
58
+ custom_dataset=custom_dataset["train"]
59
+ def batch_iterator():
60
+ for idx in range(0, len(custom_dataset), args.batch_size):
61
+ yield custom_dataset[idx: idx + args.batch_size]['text']
62
+
63
+ if args.use_config == 'vanilla':
64
+ tokenizer = Tokenizer(models.BPE())
65
+ tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
66
+ print(f"[INFO] The brown fox jumped over the lazy dog\n{tokenizer.pre_tokenizer.pre_tokenize_str('The brown fox jumped over the lazy dog')}")
67
+ print(f"[INFO] Training...")
68
+ trainer = trainers.BpeTrainer(vocab_size=args.vocab_size, special_tokens=["<|endoftext|>"])
69
+ tokenizer.train_from_iterator(batch_iterator(), trainer=trainer)
70
+ tokenizer.post_processor = processors.ByteLevel(trim_offsets=False)
71
+ tokenizer.decoder = decoders.ByteLevel()
72
+ tokenizer = GPT2TokenizerFast(tokenizer_object=tokenizer)
73
+ elif args.use_config == 'xlm-roberta':
74
+ print("skipped")
75
+ tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base')
76
+ trained_tokenizer = tokenizer.train_new_from_iterator(batch_iterator(), vocab_size=args.vocab_size)
77
+ elif args.use_config == 'gemma':
78
+ print("skipped")
79
+ tokenizer = AutoTokenizer.from_pretrained('hf-internal-testing/dummy-gemma')
80
+ trained_tokenizer = tokenizer.train_new_from_iterator(batch_iterator(), vocab_size=args.vocab_size)
81
+
82
+ trained_tokenizer.save_pretrained('hi-indiccorp-gemma-bgpt-bpe-tokenizer1')
83
+ print(f"[INFO] Tokenizer saved to disk")
84
+
85
+
86
+
87
+
88
+ # test_hi= load_dataset('ai4bharat/samanantar', 'hi', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
89
+ # test_bn= load_dataset('ai4bharat/samanantar', 'bn', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE')
90
+ # print("-------------------------------")
91
+ # print(test_hi)
92
+ # print(test_bn)
93
+ # print("-------------------------------")
94
+ # print(len(test_hi["tgt"]))
95
+ # print(len(test_bn["tgt"]))
96
+ def main():
97
+ args, _ = parse_arguments()
98
+ train_tokenizer(args)
99
+
100
+ if __name__ == "__main__":
101
+ main()
102
+
train_sanghara_multilingual_tokenizer.py ADDED
@@ -0,0 +1,109 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from datasets import concatenate_datasets, load_dataset, load_from_disk
2
+ import argparse
3
+ from tokenizers import Tokenizer, decoders, models, pre_tokenizers, processors, trainers
4
+ from transformers import GPT2TokenizerFast, AutoTokenizer
5
+ from datasets import config
6
+ from datasets import DatasetDict, Dataset
7
+ import logging
8
+
9
+ def initialize_logger(log_file):
10
+ logging.basicConfig(filename=log_file, level=logging.INFO, format='%(asctime)s: %(message)s')
11
+
12
+ def log_parameters(vocab_size, batch_size, fertility_score, proportion_continued_words, log_file='parameters.log'):
13
+ initialize_logger(log_file)
14
+ logging.info(f"Vocabulary Size: {vocab_size}, Batch Size: {batch_size}, Fertility Score: {fertility_score}, Proportion of Continued word: {proportion_continued_words}")
15
+
16
+ def parse_arguments():
17
+ parser = argparse.ArgumentParser()
18
+ parser.add_argument(
19
+ "--batch_size",
20
+ type=int,
21
+ required=True,
22
+ help="Batch size to use for training"
23
+ )
24
+ parser.add_argument(
25
+ "--vocab_size",
26
+ type=int,
27
+ required=True,
28
+ help="Vocabulary size to use for tokenizer"
29
+ )
30
+ parser.add_argument(
31
+ "--use_config",
32
+ choices=['xlm-roberta', 'vanilla','llama'],
33
+ required=True,
34
+ help="Use XLM-RoBERTa config or Vanilla BPE"
35
+ )
36
+ parser.add_argument(
37
+ "--do_evaluate",
38
+ action='store_true',
39
+ help="Enable evaluation."
40
+ )
41
+ args = parser.parse_known_args()
42
+ return args
43
+ def train_tokenizer(args):
44
+
45
+ code_dataset_go= load_dataset('code_x_glue_ct_code_to_text','go',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
46
+ code_dataset_java= load_dataset('code_x_glue_ct_code_to_text','java',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
47
+ code_dataset_javascript= load_dataset('code_x_glue_ct_code_to_text','javascript',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
48
+ code_dataset_php= load_dataset('code_x_glue_ct_code_to_text','php',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
49
+ code_dataset_python= load_dataset('code_x_glue_ct_code_to_text','python',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
50
+ code_dataset_ruby= load_dataset('code_x_glue_ct_code_to_text','ruby',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
51
+
52
+ indic_datasets_hi= load_dataset('ai4bharat/sangraha', data_dir="verified/hin", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text'][:6000000]
53
+ indic_datasets_bn= load_dataset('ai4bharat/sangraha', data_dir="verified/ben", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text'][:6000000]
54
+ wikipedia_en = load_dataset("wikipedia", "20220301.en", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text'][:1000000]
55
+
56
+
57
+ combined_train_set=code_dataset_go+code_dataset_java+code_dataset_javascript+code_dataset_php+code_dataset_python+code_dataset_ruby+indic_datasets_hi+indic_datasets_bn+wikipedia_en
58
+
59
+ data = {
60
+ "train":{"text": combined_train_set},
61
+ "validation": {"text": []},
62
+ "test": {"text": []},
63
+ }
64
+ # print(data)
65
+ custom_dataset = DatasetDict()
66
+ for split in data:
67
+ custom_dataset[split] = Dataset.from_dict(data[split])
68
+ custom_dataset=custom_dataset["train"]
69
+
70
+ def batch_iterator():
71
+ for idx in range(0, len(custom_dataset), args.batch_size):
72
+ yield custom_dataset[idx: idx + args.batch_size]['text']
73
+
74
+ new_line="\n"
75
+ replacing_dict={}
76
+ for i in range(5,25):
77
+ replacecable_token="<|reserved_special_token_"+str(i)+"|>"
78
+ replacing_dict[replacecable_token]=new_line
79
+ new_line+="\n"
80
+ if args.use_config == 'vanilla':
81
+ tokenizer = Tokenizer(models.BPE())
82
+ tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
83
+ print(f"[INFO] The brown fox jumped over the lazy dog\n{tokenizer.pre_tokenizer.pre_tokenize_str('The brown fox jumped over the lazy dog')}")
84
+ print(f"[INFO] Training...")
85
+ trainer = trainers.BpeTrainer(vocab_size=args.vocab_size, special_tokens=["<|endoftext|>"])
86
+ tokenizer.train_from_iterator(batch_iterator(), trainer=trainer)
87
+ tokenizer.post_processor = processors.ByteLevel(trim_offsets=False)
88
+ tokenizer.decoder = decoders.ByteLevel()
89
+ tokenizer = GPT2TokenizerFast(tokenizer_object=tokenizer)
90
+ elif args.use_config == 'xlm-roberta':
91
+ tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base')
92
+ trained_tokenizer = tokenizer.train_new_from_iterator(batch_iterator(), vocab_size=args.vocab_size)
93
+ elif args.use_config == 'llama':
94
+ print("skipped")
95
+ tokenizer = AutoTokenizer.from_pretrained('meta-llama/Meta-Llama-3-8B')
96
+ print(tokenizer)
97
+ trained_tokenizer = tokenizer.train_new_from_iterator(batch_iterator(), vocab_size=args.vocab_size, new_special_tokens=["<unk>","<pad>","<mask>"],special_tokens_map=replacing_dict)
98
+
99
+ trained_tokenizer.save_pretrained('hi-sanghara-xlmr-bgpt-bpe-tokenizer1')
100
+ print(f"[INFO] Tokenizer saved to disk")
101
+
102
+
103
+ def main():
104
+ args, _ = parse_arguments()
105
+ train_tokenizer(args)
106
+
107
+ if __name__ == "__main__":
108
+ main()
109
+
train_sangra_multilingual_thaokenizer.py ADDED
@@ -0,0 +1,130 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from datasets import concatenate_datasets, load_dataset, load_from_disk
2
+ import argparse
3
+ from tokenizers import Tokenizer, decoders, models, pre_tokenizers, processors, trainers
4
+ from transformers import GPT2TokenizerFast, AutoTokenizer
5
+ from datasets import config
6
+ from datasets import DatasetDict, Dataset
7
+ import logging
8
+ import re
9
+ import nltk
10
+ nltk.download('punkt')
11
+
12
+ def initialize_logger(log_file):
13
+ logging.basicConfig(filename=log_file, level=logging.INFO, format='%(asctime)s: %(message)s')
14
+
15
+ def log_parameters(vocab_size, batch_size, fertility_score, proportion_continued_words, log_file='parameters.log'):
16
+ initialize_logger(log_file)
17
+ logging.info(f"Vocabulary Size: {vocab_size}, Batch Size: {batch_size}, Fertility Score: {fertility_score}, Proportion of Continued word: {proportion_continued_words}")
18
+
19
+ def parse_arguments():
20
+ parser = argparse.ArgumentParser()
21
+ parser.add_argument(
22
+ "--batch_size",
23
+ type=int,
24
+ required=True,
25
+ help="Batch size to use for training"
26
+ )
27
+ parser.add_argument(
28
+ "--vocab_size",
29
+ type=int,
30
+ required=True,
31
+ help="Vocabulary size to use for tokenizer"
32
+ )
33
+ parser.add_argument(
34
+ "--use_config",
35
+ choices=['xlm-roberta', 'vanilla','llama'],
36
+ required=True,
37
+ help="Use XLM-RoBERTa config or Vanilla BPE"
38
+ )
39
+ parser.add_argument(
40
+ "--do_evaluate",
41
+ action='store_true',
42
+ help="Enable evaluation."
43
+ )
44
+ args = parser.parse_known_args()
45
+ return args
46
+ def train_tokenizer(args):
47
+
48
+ code_dataset_go= load_dataset('code_x_glue_ct_code_to_text','go',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
49
+ code_dataset_java= load_dataset('code_x_glue_ct_code_to_text','java',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
50
+ code_dataset_javascript= load_dataset('code_x_glue_ct_code_to_text','javascript',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
51
+ code_dataset_php= load_dataset('code_x_glue_ct_code_to_text','php',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
52
+ code_dataset_python= load_dataset('code_x_glue_ct_code_to_text','python',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
53
+ code_dataset_ruby= load_dataset('code_x_glue_ct_code_to_text','ruby',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
54
+
55
+ indic_datasets_hi= load_dataset('ai4bharat/sangraha', data_dir="verified/hin", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text'][:1000000]
56
+ indic_datasets_bn= load_dataset('ai4bharat/sangraha', data_dir="verified/ben", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text'][:1000000]
57
+ wikipedia_en = load_dataset("wikipedia", "20220301.en", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text'][:1000000]
58
+ joined_string = '. '.join(indic_datasets_hi + indic_datasets_bn + wikipedia_en)
59
+ sentences = nltk.sent_tokenize(joined_string)
60
+
61
+ # Set to store sentences with a maximum of 100 words each
62
+ set_of_sentences = set()
63
+
64
+ # Iterate through the sentences to form sets with a maximum of 100 words each
65
+ current_sentence = ""
66
+ for sentence in sentences:
67
+ words = nltk.word_tokenize(sentence)
68
+ if len(current_sentence.split()) + len(words) <= 100:
69
+ current_sentence += " " + sentence
70
+ else:
71
+ set_of_sentences.add(current_sentence.strip())
72
+ current_sentence = sentence
73
+
74
+ # Add the last sentence
75
+ if current_sentence:
76
+ set_of_sentences.add(current_sentence.strip())
77
+
78
+ combined_train_set=code_dataset_go+code_dataset_java+code_dataset_javascript+code_dataset_php+code_dataset_python+code_dataset_ruby+set_of_sentences
79
+
80
+ data = {
81
+ "train":{"text": combined_train_set},
82
+ "validation": {"text": []},
83
+ "test": {"text": []},
84
+ }
85
+ # print(data)
86
+ custom_dataset = DatasetDict()
87
+ for split in data:
88
+ custom_dataset[split] = Dataset.from_dict(data[split])
89
+ custom_dataset=custom_dataset["train"]
90
+
91
+ def batch_iterator():
92
+ for idx in range(0, len(custom_dataset), args.batch_size):
93
+ yield custom_dataset[idx: idx + args.batch_size]['text']
94
+
95
+ new_line="\n"
96
+ replacing_dict={}
97
+ for i in range(5,25):
98
+ replacecable_token="<|reserved_special_token_"+str(i)+"|>"
99
+ replacing_dict[replacecable_token]=new_line
100
+ new_line+="\n"
101
+ if args.use_config == 'vanilla':
102
+ tokenizer = Tokenizer(models.BPE())
103
+ tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
104
+ print(f"[INFO] The brown fox jumped over the lazy dog\n{tokenizer.pre_tokenizer.pre_tokenize_str('The brown fox jumped over the lazy dog')}")
105
+ print(f"[INFO] Training...")
106
+ trainer = trainers.BpeTrainer(vocab_size=args.vocab_size, special_tokens=["<|endoftext|>"])
107
+ tokenizer.train_from_iterator(batch_iterator(), trainer=trainer)
108
+ tokenizer.post_processor = processors.ByteLevel(trim_offsets=False)
109
+ tokenizer.decoder = decoders.ByteLevel()
110
+ tokenizer = GPT2TokenizerFast(tokenizer_object=tokenizer)
111
+ elif args.use_config == 'xlm-roberta':
112
+ tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base')
113
+ trained_tokenizer = tokenizer.train_new_from_iterator(batch_iterator(), vocab_size=args.vocab_size)
114
+ elif args.use_config == 'llama':
115
+ print("skipped")
116
+ tokenizer = AutoTokenizer.from_pretrained('meta-llama/Meta-Llama-3-8B')
117
+ print(tokenizer)
118
+ trained_tokenizer = tokenizer.train_new_from_iterator(batch_iterator(), vocab_size=args.vocab_size, new_special_tokens=["<unk>","<pad>","<mask>"],special_tokens_map=replacing_dict)
119
+
120
+ trained_tokenizer.save_pretrained('hi-sanghara-xlmr-bgpt-bpe-tokenizer1')
121
+ print(f"[INFO] Tokenizer saved to disk")
122
+
123
+
124
+ def main():
125
+ args, _ = parse_arguments()
126
+ train_tokenizer(args)
127
+
128
+ if __name__ == "__main__":
129
+ main()
130
+
train_spm.py ADDED
@@ -0,0 +1,76 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import sentencepiece as spm
2
+
3
+
4
+ spm.SentencePieceTrainer.Train(
5
+ input = 'all.txt',
6
+ model_prefix = 'all',
7
+ vocab_size = 50304,
8
+ pad_id = 0,
9
+ unk_id = 1,
10
+ bos_id = 2,
11
+ eos_id = 3,
12
+ pad_piece = '<pad>',
13
+ unk_piece = '<unk>',
14
+ bos_piece = '<bos>',
15
+ eos_piece = '<eos>',
16
+ model_type = 'bpe',
17
+ num_threads = 256,
18
+ add_dummy_prefix = False,
19
+ byte_fallback = True,
20
+ character_coverage = 0.9999,
21
+ user_defined_symbols ='<pad>,<eos>,<bos>,<unk>,<mask>,<unused0>,<unused1>,<unused2>,<unused3>,<unused4>,<unused5>,<unused6>,<unused7>,<unused8>,<unused9>,<unused10>,<unused11>,<unused12>,<unused13>,<unused14>,<unused15>,<unused16>,<unused17>,<unused18>,<unused19>,<unused20>,<unused21>,<unused22>,<unused23>,<unused24>,<unused25>,<unused26>,<unused27>,<unused28>,<unused29>,<unused30>,<unused31>,<unused32>,<unused33>,<unused34>,<unused35>,<unused36>,<unused37>,<unused38>,<unused39>,<unused40>,<unused41>,<unused42>,<unused43>,<unused44>,<unused45>,<unused46>,<unused47>,<unused48>,<unused49>,<unused50>,<unused51>,<unused52>,<unused53>,<unused54>,<unused55>,<unused56>,<unused57>,<unused58>,<unused59>,<unused60>,<unused61>,<unused62>,<unused63>,<unused64>,<unused65>,<unused66>,<unused67>,<unused68>,<unused69>,<unused70>,<unused71>,<unused72>,<unused73>,<unused74>,<unused75>,<unused76>,<unused77>,<unused78>,<unused79>,<unused80>,<unused81>,<unused82>,<unused83>,<unused84>,<unused85>,<unused86>,<unused87>,<unused88>,<unused89>,<unused90>,<unused91>,<unused92>,<unused93>,<unused94>,<unused95>,<unused96>,<unused97>,<unused98>,<start_of_turn>,<end_of_turn>'
22
+
23
+
24
+ )
25
+
26
+ exit()
27
+
28
+ # from datasets import concatenate_datasets, load_dataset, load_from_disk
29
+ # from datasets import DatasetDict, Dataset
30
+ # import random
31
+
32
+ # def train_tokenizer(args):
33
+
34
+ code_dataset_go= load_dataset('code_x_glue_ct_code_to_text','go',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
35
+ code_dataset_java= load_dataset('code_x_glue_ct_code_to_text','java',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
36
+ code_dataset_javascript= load_dataset('code_x_glue_ct_code_to_text','javascript',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
37
+ code_dataset_php= load_dataset('code_x_glue_ct_code_to_text','php',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
38
+ code_dataset_python= load_dataset('code_x_glue_ct_code_to_text','python',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
39
+ code_dataset_ruby= load_dataset('code_x_glue_ct_code_to_text','ruby',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
40
+
41
+ # indic_datasets_hi= load_dataset('ai4bharat/sangraha', data_dir="verified/hin", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
42
+ # indic_datasets_bn= load_dataset('ai4bharat/sangraha', data_dir="verified/ben", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
43
+ # wikipedia_en = load_dataset("wikipedia", "20220301.en", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text']
44
+
45
+ # num_docs = 100000
46
+ # hi = random.sample(indic_datasets_hi, num_docs)
47
+ # bn = random.sample(indic_datasets_bn, num_docs)
48
+ # en = random.sample(wikipedia_en, num_docs)
49
+
50
+
51
+ # # combined_train_set = wikipedia_en
52
+
53
+ # # combined_train_set=code_dataset_go+code_dataset_java+code_dataset_javascript+code_dataset_php+code_dataset_python+code_dataset_ruby+indic_datasets_hi + indic_datasets_bn + wikipedia_en
54
+ # combined_train_set= en + hi + bn
55
+
56
+
57
+
58
+ # data = {
59
+ # "train":{"text": combined_train_set},
60
+ # "validation": {"text": []},
61
+ # "test": {"text": []},
62
+ # }
63
+ # # print(data)
64
+ # custom_dataset = DatasetDict()
65
+ # for split in data:
66
+ # custom_dataset[split] = Dataset.from_dict(data[split])
67
+ # custom_dataset=custom_dataset["train"]
68
+ # # lines = []
69
+
70
+ # with open('all.txt','w') as f:
71
+ # for text in custom_dataset['text']:
72
+ # lines = text.split("\n")
73
+ # for l in lines:
74
+ # f.write(l.strip() + '\n')
75
+ # # break
76
+
trained-tokenizer/convert_tokenizer.py ADDED
@@ -0,0 +1,81 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import sentencepiece as spm
2
+ from tokenizers import Tokenizer, normalizers, pre_tokenizers, decoders, trainers, models
3
+ from tokenizers.models import BPE, Unigram
4
+ from transformers import PreTrainedTokenizerFast, convert_slow_tokenizer
5
+ import warnings
6
+ from typing import Dict, List, Tuple
7
+
8
+ from packaging import version
9
+ from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
10
+ from tokenizers.models import BPE, Unigram, WordPiece
11
+
12
+ def _get_prepend_scheme(add_prefix_space: bool, original_tokenizer) -> str:
13
+ if add_prefix_space:
14
+ prepend_scheme = "always"
15
+ if not getattr(original_tokenizer, "legacy", True):
16
+ prepend_scheme = "first"
17
+ else:
18
+ prepend_scheme = "never"
19
+ return prepend_scheme
20
+
21
+ class SpmConverter2(convert_slow_tokenizer.SpmConverter):
22
+ def __init__(self, *args):
23
+ convert_slow_tokenizer.requires_backends(self, "protobuf")
24
+
25
+ super().__init__(*args)
26
+
27
+ # from .utils import sentencepiece_model_pb2 as model_pb2
28
+ model_pb2 = convert_slow_tokenizer.import_protobuf()
29
+
30
+ m = model_pb2.ModelProto()
31
+ with open(self.original_tokenizer.vocab_file, "rb") as f:
32
+ m.ParseFromString(f.read())
33
+ self.proto = m
34
+
35
+ if self.proto.trainer_spec.byte_fallback:
36
+ if not getattr(self, "handle_byte_fallback", None):
37
+ warnings.warn(
38
+ "The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"
39
+ " which is not implemented in the fast tokenizers. In practice this means that the fast version of the"
40
+ " tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these "
41
+ "unknown tokens into a sequence of byte tokens matching the original piece of text."
42
+ )
43
+
44
+ def tokenizer(self, proto):
45
+ model_type = proto.trainer_spec.model_type
46
+ vocab_scores = self.vocab(proto)
47
+ unk_id = self.unk_id(proto)
48
+
49
+ _, merges = convert_slow_tokenizer.SentencePieceExtractor(self.original_tokenizer.vocab_file).extract()
50
+ bpe_vocab = {word: i for i, (word, score) in enumerate(vocab_scores)}
51
+ tokenizer = Tokenizer(
52
+ BPE(
53
+ bpe_vocab,
54
+ merges,
55
+ unk_token=proto.trainer_spec.unk_piece,
56
+ fuse_unk=True,
57
+ )
58
+ )
59
+
60
+ return tokenizer
61
+
62
+ # Usage
63
+ spm_tokenizer = spm.SentencePieceProcessor(model_file="/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k/all.model")
64
+ spm_tokenizer.vocab_file = "/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k/all.model"
65
+ spm_converter = SpmConverter2(spm_tokenizer)
66
+ converted = spm_converter.converted()
67
+ converted.save('/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/converted.json')
68
+
69
+ tok = PreTrainedTokenizerFast(
70
+ tokenizer_file='/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/converted.json',
71
+ clean_up_tokenization_spaces=False,
72
+ pad_token='<PAD>',
73
+ unk_token='<UNK>',
74
+ bos_token='<BOS>',
75
+ eos_token='<EOS>',
76
+ mask_token='<MASK>',
77
+ model_max_length=1024,
78
+ padding_side='right',
79
+ truncation_side='right'
80
+ )
81
+ tok.save_pretrained('/mnt/weka/peacock/tokenization/trained-tokenizer/enhiben_50k_hf/ConvertedTokenizer')
trained-tokenizer/dravid_64k/ta_te_kan_ml_50kspm_tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:00973ed5449fdba701c90de28f03d4ca5d63fd2111cfcc2396fdc99180685014
3
+ size 1719743
trained-tokenizer/dravid_64k/ta_te_kan_ml_50kspm_tokenizer.vocab ADDED
The diff for this file is too large to render. See raw diff