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aatherton2024
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hf_synth_trans

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aatherton2024 commited on
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restructure

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Files changed (44) hide show
  1. eng-nah-svo-cpt/special_tokens_map.json +105 -3
  2. eng-nah-svo-cpt/tokenizer.json +0 -0
  3. eng-nah-svo-cpt/tokenizer_config.json +108 -6
  4. eng-nah-svo-translation/README.md +3 -3
  5. eng-nah-svo-translation/added_tokens.json +2 -1
  6. eng-nah-svo-translation/checkpoint-228/added_tokens.json +2 -1
  7. eng-nah-svo-translation/checkpoint-228/optimizer.pt +1 -1
  8. eng-nah-svo-translation/checkpoint-228/pytorch_model.bin +1 -1
  9. eng-nah-svo-translation/checkpoint-228/rng_state.pth +1 -1
  10. eng-nah-svo-translation/checkpoint-228/special_tokens_map.json +105 -4
  11. eng-nah-svo-translation/checkpoint-228/tokenizer.json +0 -0
  12. eng-nah-svo-translation/checkpoint-228/tokenizer_config.json +108 -6
  13. eng-nah-svo-translation/checkpoint-228/trainer_state.json +1 -1
  14. eng-nah-svo-translation/checkpoint-228/training_args.bin +1 -1
  15. eng-nah-svo-translation/checkpoint-456/added_tokens.json +2 -1
  16. eng-nah-svo-translation/checkpoint-456/optimizer.pt +1 -1
  17. eng-nah-svo-translation/checkpoint-456/pytorch_model.bin +1 -1
  18. eng-nah-svo-translation/checkpoint-456/rng_state.pth +1 -1
  19. eng-nah-svo-translation/checkpoint-456/special_tokens_map.json +105 -4
  20. eng-nah-svo-translation/checkpoint-456/tokenizer.json +0 -0
  21. eng-nah-svo-translation/checkpoint-456/tokenizer_config.json +108 -6
  22. eng-nah-svo-translation/checkpoint-456/trainer_state.json +1 -1
  23. eng-nah-svo-translation/checkpoint-456/training_args.bin +1 -1
  24. eng-nah-svo-translation/checkpoint-684/added_tokens.json +2 -1
  25. eng-nah-svo-translation/checkpoint-684/optimizer.pt +1 -1
  26. eng-nah-svo-translation/checkpoint-684/pytorch_model.bin +1 -1
  27. eng-nah-svo-translation/checkpoint-684/rng_state.pth +1 -1
  28. eng-nah-svo-translation/checkpoint-684/special_tokens_map.json +105 -4
  29. eng-nah-svo-translation/checkpoint-684/tokenizer.json +0 -0
  30. eng-nah-svo-translation/checkpoint-684/tokenizer_config.json +108 -6
  31. eng-nah-svo-translation/checkpoint-684/trainer_state.json +2 -2
  32. eng-nah-svo-translation/checkpoint-684/training_args.bin +1 -1
  33. eng-nah-svo-translation/pytorch_model.bin +1 -1
  34. eng-nah-svo-translation/special_tokens_map.json +105 -4
  35. eng-nah-svo-translation/tokenizer.json +0 -0
  36. eng-nah-svo-translation/tokenizer_config.json +108 -6
  37. eng-nah-svo-translation/training_args.bin +1 -1
  38. myerrors_1600.out +0 -49
  39. myerrors_1601.out +0 -16
  40. myerrors_1624.out +24 -0
  41. myoutput_1600.out +0 -8
  42. myoutput_1601.out +0 -9
  43. myoutput_1624.out +9 -0
  44. synth_translation.py +105 -124
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eng-nah-svo-translation/README.md CHANGED
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  This model was trained from scratch on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2640
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- - Bleu: 0.0231
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- - Chrf: 26.4891
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  ## Model description
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  This model was trained from scratch on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1119
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+ - Bleu: 0.0
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+ - Chrf: 82.3094
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  ## Model description
24
 
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- /mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
380
- warnings.warn(
381
- /mnt/storage/aatherton/hf_eng_fra_trans is already a clone of https://huggingface.co/aatherton2024/hf_eng_fra_trans. Make sure you pull the latest changes with `repo.git_pull()`.
382
- Traceback (most recent call last):
383
- File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/huggingface_hub-0.17.1-py3.8.egg/huggingface_hub/utils/_errors.py", line 261, in hf_raise_for_status
384
- File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/requests/models.py", line 1021, in raise_for_status
385
- raise HTTPError(http_error_msg, response=self)
386
- requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/aatherton2024/hf_synth_trans/resolve/main/config.json
387
-
388
- The above exception was the direct cause of the following exception:
389
-
390
- Traceback (most recent call last):
391
- File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/utils/hub.py", line 428, in cached_file
392
- resolved_file = hf_hub_download(
393
- ^^^^^^^^^^^^^^^^
394
- File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/huggingface_hub-0.17.1-py3.8.egg/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
395
- File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/huggingface_hub-0.17.1-py3.8.egg/huggingface_hub/file_download.py", line 1230, in hf_hub_download
396
- File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/huggingface_hub-0.17.1-py3.8.egg/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
397
- File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/huggingface_hub-0.17.1-py3.8.egg/huggingface_hub/file_download.py", line 1606, in get_hf_file_metadata
398
- File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/huggingface_hub-0.17.1-py3.8.egg/huggingface_hub/utils/_errors.py", line 271, in hf_raise_for_status
399
- huggingface_hub.utils._errors.EntryNotFoundError: 404 Client Error. (Request ID: Root=1-6508f53c-403d6b2e4c215eb0090d4b33;cdf95f5b-24d2-49b8-9434-cfaa697f931b)
400
-
401
- Entry Not Found for url: https://huggingface.co/aatherton2024/hf_synth_trans/resolve/main/config.json.
402
-
403
- The above exception was the direct cause of the following exception:
404
-
405
- Traceback (most recent call last):
406
- File "/mnt/storage/aatherton/hf_synth_trans/synth_translation.py", line 268, in <module>
407
- translator = pipeline("translation", model=model_checkpoint)
408
- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
409
- File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/pipelines/__init__.py", line 724, in pipeline
410
- config = AutoConfig.from_pretrained(model, _from_pipeline=task, **hub_kwargs, **model_kwargs)
411
- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
412
- File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/models/auto/configuration_auto.py", line 1007, in from_pretrained
413
- config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
414
- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
415
- File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/configuration_utils.py", line 620, in get_config_dict
416
- config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
417
- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
418
- File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/configuration_utils.py", line 675, in _get_config_dict
419
- resolved_config_file = cached_file(
420
- ^^^^^^^^^^^^
421
- File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/utils/hub.py", line 479, in cached_file
422
- raise EnvironmentError(
423
- OSError: aatherton2024/hf_synth_trans does not appear to have a file named config.json. Checkout 'https://huggingface.co/aatherton2024/hf_synth_trans/main' for available files.
 
 
 
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myerrors_1601.out DELETED
@@ -1,16 +0,0 @@
1
- You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
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- /mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
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- warnings.warn(
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- /mnt/storage/aatherton/hf_eng_fra_trans is already a clone of https://huggingface.co/aatherton2024/hf_eng_fra_trans. Make sure you pull the latest changes with `repo.git_pull()`.
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myerrors_1624.out ADDED
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1
+
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+
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+
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+ You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
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382
+
383
+
384
+ Traceback (most recent call last):
385
+ File "/mnt/storage/aatherton/hf_synth_trans/synth_translation.py", line 136, in <module>
386
+
387
+ File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/pipelines/__init__.py", line 904, in pipeline
388
+ tokenizer = AutoTokenizer.from_pretrained(
389
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
390
+ File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/models/auto/tokenization_auto.py", line 727, in from_pretrained
391
+ return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
392
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
393
+ File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 1854, in from_pretrained
394
+ return cls._from_pretrained(
395
+ ^^^^^^^^^^^^^^^^^^^^^
396
+ File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 2066, in _from_pretrained
397
+ raise ValueError(
398
+ ValueError: Wrong index found for <pad>: should be 0 but found 259.
myoutput_1600.out DELETED
@@ -1,8 +0,0 @@
1
- evaluate1
2
- {'eval_loss': 0.3294467329978943, 'eval_bleu': 0.021732338702567133, 'eval_chrf': 24.996195060769814, 'eval_runtime': 70.6427, 'eval_samples_per_second': 14.17, 'eval_steps_per_second': 0.226}
3
- trainer train 1
4
- {'loss': 0.2145, 'learning_rate': 5.380116959064328e-06, 'epoch': 2.19}
5
- {'train_runtime': 56.8334, 'train_samples_per_second': 384.914, 'train_steps_per_second': 12.035, 'train_loss': 0.2377676657068799, 'epoch': 3.0}
6
- evaluate 2
7
- {'eval_loss': 0.2867695093154907, 'eval_bleu': 0.02226070832187467, 'eval_chrf': 25.804707746098266, 'eval_runtime': 69.9013, 'eval_samples_per_second': 14.32, 'eval_steps_per_second': 0.229, 'epoch': 3.0}
8
- training model now
 
 
 
 
 
 
 
 
 
myoutput_1601.out DELETED
@@ -1,9 +0,0 @@
1
- evaluate1
2
- {'eval_loss': 0.2867695093154907, 'eval_bleu': 0.02226070832187467, 'eval_chrf': 25.804707746098266, 'eval_runtime': 73.0567, 'eval_samples_per_second': 13.702, 'eval_steps_per_second': 0.219}
3
- trainer train 1
4
- {'loss': 0.1428, 'learning_rate': 5.380116959064328e-06, 'epoch': 2.19}
5
- {'train_runtime': 57.3032, 'train_samples_per_second': 381.759, 'train_steps_per_second': 11.936, 'train_loss': 0.17679918300338657, 'epoch': 3.0}
6
- evaluate 2
7
- {'eval_loss': 0.26398155093193054, 'eval_bleu': 0.02310668515912661, 'eval_chrf': 26.48909190811468, 'eval_runtime': 71.4593, 'eval_samples_per_second': 14.008, 'eval_steps_per_second': 0.224, 'epoch': 3.0}
8
- training model now
9
- [{'translation_text': 'axinquigudiflogaqueh.aqueh.aqueh.aqueh.aqueh.aqueh.aqueh.aqueh.aqueh.aquaqueh.aqueh.aqueh.aqueh.ah.ah.ah.ah.ac.ah.ac.aquac.aquac.aquaquaquaquaqabah.aqabac.aquaquaquaqababac.ac.ac.ac.ac.ac.aquac.ac.aquac.ac.aquac.caquac.ac.aqac.aqacbaqacbac.aqacaquaqaqubach.aqaqach.aqaqaquhaqaqachbquhbquququququququmabquhaquhaquququququququququququququququmababababababququququququququmabababachbachbachbacqubachchbachbobobachchchchchchchchchchchchchchchchchchchchchchchchbohbohbohdugogogogohgogogogogogogogogogogogogogogogogo'}]
 
 
 
 
 
 
 
 
 
 
myoutput_1624.out ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+
2
+
3
+ evaluate1
4
+ {'eval_loss': 0.13620370626449585, 'eval_bleu': 0.0, 'eval_chrf': 75.88572818258336, 'eval_runtime': 16.7818, 'eval_samples_per_second': 59.648, 'eval_steps_per_second': 0.953}
5
+ trainer train 1
6
+ {'loss': 0.1085, 'learning_rate': 5.380116959064328e-06, 'epoch': 2.19}
7
+ {'train_runtime': 58.0801, 'train_samples_per_second': 376.652, 'train_steps_per_second': 11.777, 'train_loss': 0.10791369488364772, 'epoch': 3.0}
8
+ evaluate 2
9
+ {'eval_loss': 0.11191454529762268, 'eval_bleu': 0.0, 'eval_chrf': 82.30937138468823, 'eval_runtime': 16.5283, 'eval_samples_per_second': 60.563, 'eval_steps_per_second': 0.968, 'epoch': 3.0}
synth_translation.py CHANGED
@@ -19,22 +19,35 @@ import torch
19
  from torch import Tensor
20
  import os
21
 
22
- #load in dataset, setup tokenizer
23
-
24
- def addperiod(entry):
25
- entry['en'] += '.'
26
- entry['fr'] += '.'
27
- return entry
28
-
29
- raw_datasets = load_dataset("aatherton2024/eng-nah-svo")
30
- train_ds = raw_datasets['train'].map(addperiod)
31
- validation_ds = raw_datasets['validation'].map(addperiod)
32
- test_ds = raw_datasets['test'].map(addperiod)
33
-
34
- raw_datasets = DatasetDict({"train" : train_ds, "validation" : validation_ds, "test" : test_ds})
35
- model_checkpoint = "eng-nah-svo-cpt"
 
 
 
 
 
 
 
 
 
36
 
37
- if False: #data processing only needs to run once
 
 
 
 
38
  def get_training_corpus(raw_datasets):
39
  return (
40
  raw_datasets["train"][i : i + 1000]
@@ -42,50 +55,31 @@ if False: #data processing only needs to run once
42
  )
43
 
44
  training_corpus = get_training_corpus(raw_datasets)
45
- old_tokenizer = AutoTokenizer.from_pretrained("gpt2")
46
  tokenizer = old_tokenizer.train_new_from_iterator(training_corpus, 52000)
47
-
48
- tokenizer.save_pretrained("eng-nah-svo-cpt")
49
- tokenizer.push_to_hub("eng-nah-svo-cpt")
50
-
51
- max_length = 128
52
- tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
53
- tokenizer.add_special_tokens({'pad_token': '[PAD]'})
54
 
55
  #scan dataset, storing lists of english and french words then returning the tokenization of them
56
  def preprocess_function(examples):
57
- inputs = examples["en"]
58
- targets = examples["fr"]
 
59
  model_inputs = tokenizer(
60
- inputs, text_target=targets, max_length=max_length, truncation=True
61
  )
62
  return model_inputs
63
 
64
  #apply preprocessing in one go to all splits of the dataset
65
- tokenized_datasets = raw_datasets.map(
66
- preprocess_function,
67
- batched=True,
68
- remove_columns=raw_datasets["train"].column_names
69
- )
70
-
71
- # #model choice for this problem
72
- if False: #load pretrained model
73
- model = AutoModelForSeq2SeqLM.from_pretrained("eng-nah-svo-translation")
74
-
75
- else:
76
- from transformers import BertConfig, BertLMHeadModel
77
- from transformers import AutoModel
78
-
79
- model = AutoModelForSeq2SeqLM.from_pretrained("eng-nah-svo-translation")
80
-
81
-
82
-
83
- #data collator takes tokenizer and the model to deal with padding for dynamic batching
84
- data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
85
-
86
- #Using BLEU as our metric for this problem
87
- metric_bleu = evaluate.load("sacrebleu")
88
- metric_chrf = evaluate.load("chrf")
89
 
90
  #simple method to return test metrics
91
  def compute_metrics(eval_preds):
@@ -104,33 +98,20 @@ def compute_metrics(eval_preds):
104
  decoded_preds = [pred.strip() for pred in decoded_preds]
105
  decoded_labels = [[label.strip()] for label in decoded_labels]
106
 
107
- result_bleu = metric_bleu.compute(predictions=decoded_preds, references=decoded_labels)
108
- result_chrf = metric_chrf.compute(predictions=decoded_preds, references=decoded_labels)
109
  return {"bleu": result_bleu["score"], "chrf": result_chrf["score"]}
110
 
111
- ### We now enter the fine-tuning phase of our model structure ###
112
-
113
-
114
- #definition of seq2seq training arguments --- figure what these are/use case
115
- args = Seq2SeqTrainingArguments(
116
- f"eng-nah-svo-translation",
117
- evaluation_strategy="no",
118
- save_strategy="epoch",
119
- learning_rate=2e-5,
120
- per_device_train_batch_size=32,
121
- per_device_eval_batch_size=64,
122
- weight_decay=0.01,
123
- save_total_limit=3,
124
- num_train_epochs=3,
125
- predict_with_generate=True,
126
- fp16=False,
127
- push_to_hub=True,
128
- )
129
 
130
- #pass all information to trainer
131
  trainer = Seq2SeqTrainer(
132
  model,
133
- args,
134
  train_dataset=tokenized_datasets["train"],
135
  eval_dataset=tokenized_datasets["test"],
136
  data_collator=data_collator,
@@ -139,74 +120,80 @@ trainer = Seq2SeqTrainer(
139
  )
140
 
141
  print("evaluate1")
142
- print(trainer.evaluate(max_length=max_length))
143
  print("trainer train 1")
144
  trainer.train()
145
  print("evaluate 2")
146
- print(trainer.evaluate(max_length=max_length))
147
  trainer.push_to_hub(tags="translation", commit_message="Training complete")
148
- print("training model now")
149
- model.train()
150
 
 
 
 
 
 
 
151
 
152
- tokenized_datasets.set_format("torch")
153
- train_dataloader = DataLoader(
154
- tokenized_datasets["train"],
155
- shuffle=True,
156
- collate_fn=data_collator,
157
- batch_size=8,
158
- )
159
- eval_dataloader = DataLoader(
160
- tokenized_datasets["test"], collate_fn=data_collator, batch_size=8, drop_last=True
161
- )
162
 
163
- model = AutoModelForSeq2SeqLM.from_pretrained("eng-nah-svo-translation")
164
 
 
 
 
 
 
 
 
 
 
 
165
 
166
- optimizer = AdamW(model.parameters(), lr=2e-5)
167
 
168
 
 
169
 
170
- accelerator = Accelerator()
171
- model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
172
- model, optimizer, train_dataloader, eval_dataloader
173
- )
174
 
175
 
 
 
 
 
176
 
177
- num_train_epochs = 3
178
- num_update_steps_per_epoch = len(train_dataloader)
179
- num_training_steps = num_train_epochs * num_update_steps_per_epoch
180
 
181
- lr_scheduler = get_scheduler(
182
- "linear",
183
- optimizer=optimizer,
184
- num_warmup_steps=0,
185
- num_training_steps=num_training_steps,
186
- )
187
 
 
 
 
188
 
 
 
 
 
 
 
189
 
190
- model_name = "model"
191
 
192
- output_dir = "./output"
193
- repo = Repository("/mnt/storage/aatherton/hf_eng_fra_trans", clone_from="aatherton2024/hf_eng_fra_trans")
194
 
 
195
 
196
- def postprocess(predictions, labels):
197
- predictions = predictions.cpu().numpy()
198
- labels = labels.cpu().numpy()
199
 
200
- decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
201
 
202
- # Replace -100 in the labels as we can't decode them.
203
- labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
204
- decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
205
 
206
- # Some simple post-processing
207
- decoded_preds = [pred.strip() for pred in decoded_preds]
208
- decoded_labels = [[label.strip()] for label in decoded_labels]
209
- return decoded_preds, decoded_labels
 
 
 
 
 
 
210
 
211
 
212
 
@@ -232,7 +219,7 @@ def postprocess(predictions, labels):
232
  # generated_tokens = accelerator.unwrap_model(model).generate(
233
  # batch["input_ids"],
234
  # attention_mask=batch["attention_mask"],
235
- # max_length=128,
236
  # )
237
  # labels = batch["labels"]
238
 
@@ -246,9 +233,9 @@ def postprocess(predictions, labels):
246
  # labels_gathered = accelerator.gather(labels)
247
 
248
  # decoded_preds, decoded_labels = postprocess(predictions_gathered, labels_gathered)
249
- # metric_bleu.add_batch(predictions=decoded_preds, references=decoded_labels)
250
 
251
- # results = metric_bleu.compute()
252
  # print(f"epoch {epoch}, BLEU score: {results['score']:.2f}")
253
 
254
  # # Save and upload
@@ -264,9 +251,3 @@ def postprocess(predictions, labels):
264
 
265
 
266
  # Replace this with your own checkpoint
267
- model_checkpoint = "aatherton2024/eng-nah-svo-translation"
268
- translator = pipeline("translation", model=model_checkpoint)
269
- translator("Default to expanded threads")
270
- print(translator(
271
- "you did not frichopize him"
272
- ))
 
19
  from torch import Tensor
20
  import os
21
 
22
+ #constants
23
+ MAX_LENGTH = 128
24
+ RUN_PROCESS_DATA_TOKENIZER = True
25
+ DATASET_PATH = "aatherton2024/eng-nah-svo"
26
+ TOKENIZER_CHECKPOINT = "eng-nah-svo-cpt"
27
+ MODEL_CHECKPOINT = "eng-nah-svo-translation"
28
+ PRETRAINED_MODEL = "t5-small"
29
+ METRIC_BLEU = evaluate.load("sacrebleu")
30
+ METRIC_CHRF = evaluate.load("chrf")
31
+ ARGS = Seq2SeqTrainingArguments(
32
+ f"eng-nah-svo-translation",
33
+ evaluation_strategy="no",
34
+ save_strategy="epoch",
35
+ learning_rate=2e-5,
36
+ per_device_train_batch_size=32,
37
+ per_device_eval_batch_size=64,
38
+ weight_decay=0.01,
39
+ save_total_limit=3,
40
+ num_train_epochs=3,
41
+ predict_with_generate=True,
42
+ fp16=False,
43
+ push_to_hub=True,
44
+ )
45
 
46
+ #simple method to either load tokenizer or train new one
47
+ def get_tokenizer():
48
+ if not RUN_PROCESS_DATA_TOKENIZER:
49
+ return AutoTokenizer.from_pretrained(TOKENIZER_CHECKPOINT)
50
+
51
  def get_training_corpus(raw_datasets):
52
  return (
53
  raw_datasets["train"][i : i + 1000]
 
55
  )
56
 
57
  training_corpus = get_training_corpus(raw_datasets)
58
+ old_tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL)
59
  tokenizer = old_tokenizer.train_new_from_iterator(training_corpus, 52000)
60
+ tokenizer.add_special_tokens({'pad_token': '<pad>', 'eos_token': "</s>"})
61
+ tokenizer.save_pretrained(TOKENIZER_CHECKPOINT)
62
+ tokenizer.push_to_hub(TOKENIZER_CHECKPOINT)
63
+ return tokenizer
 
 
 
64
 
65
  #scan dataset, storing lists of english and french words then returning the tokenization of them
66
  def preprocess_function(examples):
67
+ prefix = "translate en to fr"
68
+ inputs = [prefix + example for example in examples["en"]]
69
+ targets = [prefix + example for example in examples["fr"]]
70
  model_inputs = tokenizer(
71
+ inputs, text_target=targets, MAX_LENGTH=MAX_LENGTH, truncation=True, padding=True
72
  )
73
  return model_inputs
74
 
75
  #apply preprocessing in one go to all splits of the dataset
76
+ def tokenize_datasets():
77
+ tokenized_datasets = raw_datasets.map(
78
+ preprocess_function,
79
+ batched=True,
80
+ remove_columns=raw_datasets["train"].column_names
81
+ )
82
+ return tokenized_datasets
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
83
 
84
  #simple method to return test metrics
85
  def compute_metrics(eval_preds):
 
98
  decoded_preds = [pred.strip() for pred in decoded_preds]
99
  decoded_labels = [[label.strip()] for label in decoded_labels]
100
 
101
+ result_bleu = METRIC_BLEU.compute(predictions=decoded_preds, references=decoded_labels)
102
+ result_chrf = METRIC_CHRF.compute(predictions=decoded_preds, references=decoded_labels)
103
  return {"bleu": result_bleu["score"], "chrf": result_chrf["score"]}
104
 
105
+ #main testing script
106
+ raw_datasets = load_dataset("aatherton2024/eng-nah-svo")
107
+ tokenizer = get_tokenizer()
108
+ tokenized_datasets = tokenize_datasets()
109
+ model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_CHECKPOINT)
110
+ data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
 
 
 
 
 
 
 
 
 
 
 
 
111
 
 
112
  trainer = Seq2SeqTrainer(
113
  model,
114
+ ARGS,
115
  train_dataset=tokenized_datasets["train"],
116
  eval_dataset=tokenized_datasets["test"],
117
  data_collator=data_collator,
 
120
  )
121
 
122
  print("evaluate1")
123
+ print(trainer.evaluate(max_length=MAX_LENGTH))
124
  print("trainer train 1")
125
  trainer.train()
126
  print("evaluate 2")
127
+ print(trainer.evaluate(max_length=MAX_LENGTH))
128
  trainer.push_to_hub(tags="translation", commit_message="Training complete")
 
 
129
 
130
+ MODEL_CHECKPOINT = "aatherton2024/eng-nah-svo-translation"
131
+ translator = pipeline("translation", model=MODEL_CHECKPOINT)
132
+ translator("Default to expanded threads")
133
+ print(translator(
134
+ "you did not frichopize him"
135
+ ))
136
 
 
 
 
 
 
 
 
 
 
 
137
 
 
138
 
139
+ # tokenized_datasets.set_format("torch")
140
+ # train_dataloader = DataLoader(
141
+ # tokenized_datasets["train"],
142
+ # shuffle=True,
143
+ # collate_fn=data_collator,
144
+ # batch_size=8,
145
+ # )
146
+ # eval_dataloader = DataLoader(
147
+ # tokenized_datasets["test"], collate_fn=data_collator, batch_size=8, drop_last=True
148
+ # )
149
 
150
+ # model = AutoModelForSeq2SeqLM.from_pretrained("eng-nah-svo-translation")
151
 
152
 
153
+ # optimizer = AdamW(model.parameters(), lr=2e-5)
154
 
 
 
 
 
155
 
156
 
157
+ # accelerator = Accelerator()
158
+ # model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
159
+ # model, optimizer, train_dataloader, eval_dataloader
160
+ # )
161
 
 
 
 
162
 
 
 
 
 
 
 
163
 
164
+ # num_train_epochs = 3
165
+ # num_update_steps_per_epoch = len(train_dataloader)
166
+ # num_training_steps = num_train_epochs * num_update_steps_per_epoch
167
 
168
+ # lr_scheduler = get_scheduler(
169
+ # "linear",
170
+ # optimizer=optimizer,
171
+ # num_warmup_steps=0,
172
+ # num_training_steps=num_training_steps,
173
+ # )
174
 
 
175
 
 
 
176
 
177
+ # model_name = "model"
178
 
179
+ # output_dir = "./output"
180
+ # repo = Repository("/mnt/storage/aatherton/hf_eng_fra_trans", clone_from="aatherton2024/hf_eng_fra_trans")
 
181
 
 
182
 
183
+ # def postprocess(predictions, labels):
184
+ # predictions = predictions.cpu().numpy()
185
+ # labels = labels.cpu().numpy()
186
 
187
+ # decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
188
+
189
+ # # Replace -100 in the labels as we can't decode them.
190
+ # labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
191
+ # decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
192
+
193
+ # # Some simple post-processing
194
+ # decoded_preds = [pred.strip() for pred in decoded_preds]
195
+ # decoded_labels = [[label.strip()] for label in decoded_labels]
196
+ # return decoded_preds, decoded_labels
197
 
198
 
199
 
 
219
  # generated_tokens = accelerator.unwrap_model(model).generate(
220
  # batch["input_ids"],
221
  # attention_mask=batch["attention_mask"],
222
+ # MAX_LENGTH=128,
223
  # )
224
  # labels = batch["labels"]
225
 
 
233
  # labels_gathered = accelerator.gather(labels)
234
 
235
  # decoded_preds, decoded_labels = postprocess(predictions_gathered, labels_gathered)
236
+ # METRIC_BLEU.add_batch(predictions=decoded_preds, references=decoded_labels)
237
 
238
+ # results = METRIC_BLEU.compute()
239
  # print(f"epoch {epoch}, BLEU score: {results['score']:.2f}")
240
 
241
  # # Save and upload
 
251
 
252
 
253
  # Replace this with your own checkpoint