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+e n
+f r
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+  "eos_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>"
+}
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new file mode 100644
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+  "add_prefix_space": false,
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 1024,
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}
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diff --git a/eng-nah-svo-translation/README.md b/eng-nah-svo-translation/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ea189e3dfa21cee69cfcd5a4b4e1369eacb0e216
--- /dev/null
+++ b/eng-nah-svo-translation/README.md
@@ -0,0 +1,58 @@
+---
+license: apache-2.0
+base_model: Helsinki-NLP/opus-mt-en-fr
+tags:
+- translation
+- generated_from_trainer
+metrics:
+- bleu
+model-index:
+- name: eng-nah-svo-translation
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# eng-nah-svo-translation
+
+This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7505
+- Bleu: 0.0
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 32
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.0.1
+- Datasets 2.14.5
+- Tokenizers 0.13.2
diff --git a/eng-nah-svo-translation/added_tokens.json b/eng-nah-svo-translation/added_tokens.json
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+++ b/eng-nah-svo-translation/added_tokens.json
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+{
+  "[PAD]": 259
+}
diff --git a/eng-nah-svo-translation/checkpoint-228/added_tokens.json b/eng-nah-svo-translation/checkpoint-228/added_tokens.json
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+{
+  "[PAD]": 259
+}
diff --git a/eng-nah-svo-translation/checkpoint-228/config.json b/eng-nah-svo-translation/checkpoint-228/config.json
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--- /dev/null
+++ b/eng-nah-svo-translation/checkpoint-228/config.json
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+{
+  "_name_or_path": "Helsinki-NLP/opus-mt-en-fr",
+  "_num_labels": 3,
+  "activation_dropout": 0.0,
+  "activation_function": "swish",
+  "add_bias_logits": false,
+  "add_final_layer_norm": false,
+  "architectures": [
+    "MarianMTModel"
+  ],
+  "attention_dropout": 0.0,
+  "bad_words_ids": [
+    [
+      59513
+    ]
+  ],
+  "bos_token_id": 0,
+  "classif_dropout": 0.0,
+  "classifier_dropout": 0.0,
+  "d_model": 512,
+  "decoder_attention_heads": 8,
+  "decoder_ffn_dim": 2048,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 6,
+  "decoder_start_token_id": 59513,
+  "decoder_vocab_size": 59514,
+  "dropout": 0.1,
+  "encoder_attention_heads": 8,
+  "encoder_ffn_dim": 2048,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 6,
+  "eos_token_id": 0,
+  "forced_eos_token_id": 0,
+  "gradient_checkpointing": false,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "max_length": 512,
+  "max_position_embeddings": 512,
+  "model_type": "marian",
+  "normalize_before": false,
+  "normalize_embedding": false,
+  "num_beams": 4,
+  "num_hidden_layers": 6,
+  "pad_token_id": 59513,
+  "scale_embedding": true,
+  "share_encoder_decoder_embeddings": true,
+  "static_position_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.32.1",
+  "use_cache": true,
+  "vocab_size": 59514
+}
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+{
+  "bad_words_ids": [
+    [
+      59513
+    ]
+  ],
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+  "decoder_start_token_id": 59513,
+  "eos_token_id": 0,
+  "forced_eos_token_id": 0,
+  "max_length": 512,
+  "num_beams": 4,
+  "pad_token_id": 59513,
+  "renormalize_logits": true,
+  "transformers_version": "4.32.1"
+}
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+#version: 0.2 - Trained by `huggingface/tokenizers`
+e n
+f r
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+  "eos_token": "<|endoftext|>",
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+      "Ľ": 250,
+      "ľ": 251,
+      "Ŀ": 252,
+      "ŀ": 253,
+      "Ł": 254,
+      "ł": 255,
+      "Ń": 256,
+      "en": 257,
+      "fr": 258
+    },
+    "merges": [
+      "e n",
+      "f r"
+    ]
+  }
+}
\ No newline at end of file
diff --git a/eng-nah-svo-translation/tokenizer_config.json b/eng-nah-svo-translation/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..546efe6d18ae2ad7758d0c9ef51cacdb81c8dc9d
--- /dev/null
+++ b/eng-nah-svo-translation/tokenizer_config.json
@@ -0,0 +1,9 @@
+{
+  "add_prefix_space": false,
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 1024,
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}
diff --git a/eng-nah-svo-translation/training_args.bin b/eng-nah-svo-translation/training_args.bin
new file mode 100644
index 0000000000000000000000000000000000000000..8acb0269dd29c299627557f2024516755d820699
--- /dev/null
+++ b/eng-nah-svo-translation/training_args.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a721cf0d37771b1d7220d33b1f17366183f3d518b2432e040b875a92c5be520b
+size 4219
diff --git a/eng-nah-svo-translation/vocab.json b/eng-nah-svo-translation/vocab.json
new file mode 100644
index 0000000000000000000000000000000000000000..9cd5bf34d923be02a0eae563f9c595833f08bef3
--- /dev/null
+++ b/eng-nah-svo-translation/vocab.json
@@ -0,0 +1 @@
+{"<|endoftext|>":0,"!":1,"\"":2,"#":3,"$":4,"%":5,"&":6,"'":7,"(":8,")":9,"*":10,"+":11,",":12,"-":13,".":14,"/":15,"0":16,"1":17,"2":18,"3":19,"4":20,"5":21,"6":22,"7":23,"8":24,"9":25,":":26,";":27,"<":28,"=":29,">":30,"?":31,"@":32,"A":33,"B":34,"C":35,"D":36,"E":37,"F":38,"G":39,"H":40,"I":41,"J":42,"K":43,"L":44,"M":45,"N":46,"O":47,"P":48,"Q":49,"R":50,"S":51,"T":52,"U":53,"V":54,"W":55,"X":56,"Y":57,"Z":58,"[":59,"\\":60,"]":61,"^":62,"_":63,"`":64,"a":65,"b":66,"c":67,"d":68,"e":69,"f":70,"g":71,"h":72,"i":73,"j":74,"k":75,"l":76,"m":77,"n":78,"o":79,"p":80,"q":81,"r":82,"s":83,"t":84,"u":85,"v":86,"w":87,"x":88,"y":89,"z":90,"{":91,"|":92,"}":93,"~":94,"¡":95,"¢":96,"£":97,"¤":98,"¥":99,"¦":100,"§":101,"¨":102,"©":103,"ª":104,"«":105,"¬":106,"®":107,"¯":108,"°":109,"±":110,"²":111,"³":112,"´":113,"µ":114,"¶":115,"·":116,"¸":117,"¹":118,"º":119,"»":120,"¼":121,"½":122,"¾":123,"¿":124,"À":125,"Á":126,"Â":127,"Ã":128,"Ä":129,"Å":130,"Æ":131,"Ç":132,"È":133,"É":134,"Ê":135,"Ë":136,"Ì":137,"Í":138,"Î":139,"Ï":140,"Ð":141,"Ñ":142,"Ò":143,"Ó":144,"Ô":145,"Õ":146,"Ö":147,"×":148,"Ø":149,"Ù":150,"Ú":151,"Û":152,"Ü":153,"Ý":154,"Þ":155,"ß":156,"à":157,"á":158,"â":159,"ã":160,"ä":161,"å":162,"æ":163,"ç":164,"è":165,"é":166,"ê":167,"ë":168,"ì":169,"í":170,"î":171,"ï":172,"ð":173,"ñ":174,"ò":175,"ó":176,"ô":177,"õ":178,"ö":179,"÷":180,"ø":181,"ù":182,"ú":183,"û":184,"ü":185,"ý":186,"þ":187,"ÿ":188,"Ā":189,"ā":190,"Ă":191,"ă":192,"Ą":193,"ą":194,"Ć":195,"ć":196,"Ĉ":197,"ĉ":198,"Ċ":199,"ċ":200,"Č":201,"č":202,"Ď":203,"ď":204,"Đ":205,"đ":206,"Ē":207,"ē":208,"Ĕ":209,"ĕ":210,"Ė":211,"ė":212,"Ę":213,"ę":214,"Ě":215,"ě":216,"Ĝ":217,"ĝ":218,"Ğ":219,"ğ":220,"Ġ":221,"ġ":222,"Ģ":223,"ģ":224,"Ĥ":225,"ĥ":226,"Ħ":227,"ħ":228,"Ĩ":229,"ĩ":230,"Ī":231,"ī":232,"Ĭ":233,"ĭ":234,"Į":235,"į":236,"İ":237,"ı":238,"IJ":239,"ij":240,"Ĵ":241,"ĵ":242,"Ķ":243,"ķ":244,"ĸ":245,"Ĺ":246,"ĺ":247,"Ļ":248,"ļ":249,"Ľ":250,"ľ":251,"Ŀ":252,"ŀ":253,"Ł":254,"ł":255,"Ń":256,"en":257,"fr":258}
\ No newline at end of file
diff --git a/myerrors_1551.out b/myerrors_1551.out
new file mode 100644
index 0000000000000000000000000000000000000000..ab65a5a7401763e92b51f249db9d4702764cdce0
--- /dev/null
+++ b/myerrors_1551.out
@@ -0,0 +1,34 @@
+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.
+Using pad_token, but it is not set yet.
+Traceback (most recent call last):
+  File "/mnt/storage/aatherton/hf_synth_trans/synth_translation.py", line 130, in <module>
+    trainer.evaluate(max_length=max_length)
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/trainer_seq2seq.py", line 159, in evaluate
+    return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/trainer.py", line 2972, in evaluate
+    output = eval_loop(
+             ^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/trainer.py", line 3151, in evaluation_loop
+    for step, inputs in enumerate(dataloader):
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/accelerate/data_loader.py", line 384, in __iter__
+    current_batch = next(dataloader_iter)
+                    ^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/torch/utils/data/dataloader.py", line 633, in __next__
+    data = self._next_data()
+           ^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/torch/utils/data/dataloader.py", line 677, in _next_data
+    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py", line 54, in fetch
+    return self.collate_fn(data)
+           ^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/data/data_collator.py", line 586, in __call__
+    features = self.tokenizer.pad(
+               ^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 3059, in pad
+    padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
+                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 2507, in _get_padding_truncation_strategies
+    raise ValueError(
+ValueError: Asking to pad but the tokenizer does not have a padding token. Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`.
diff --git a/myerrors_1552.out b/myerrors_1552.out
new file mode 100644
index 0000000000000000000000000000000000000000..f6f885aa01e57e2d5410aa3d681f4a024427cb0c
--- /dev/null
+++ b/myerrors_1552.out
@@ -0,0 +1,29 @@
+
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+
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+
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+
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+
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+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.
+Traceback (most recent call last):
+  File "/mnt/storage/aatherton/hf_synth_trans/synth_translation.py", line 131, in <module>
+    trainer.evaluate(max_length=max_length)
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/trainer_seq2seq.py", line 159, in evaluate
+    return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/trainer.py", line 2972, in evaluate
+    output = eval_loop(
+             ^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/trainer.py", line 3161, in evaluation_loop
+    loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
+                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/trainer_seq2seq.py", line 282, in prediction_step
+    generated_tokens = self.model.generate(**inputs, **gen_kwargs)
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
+    return func(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/generation/utils.py", line 1402, in generate
+    self._validate_model_class()
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/generation/utils.py", line 1197, in _validate_model_class
+    raise TypeError(exception_message)
+TypeError: The current model class (BertModel) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'BertLMHeadModel'}
diff --git a/myerrors_1553.out b/myerrors_1553.out
new file mode 100644
index 0000000000000000000000000000000000000000..f0089959e2078c121c14d02c46798e99bb2f5bc7
--- /dev/null
+++ b/myerrors_1553.out
@@ -0,0 +1,25 @@
+
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+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.
+Traceback (most recent call last):
+  File "/mnt/storage/aatherton/hf_synth_trans/synth_translation.py", line 131, in <module>
+    trainer.evaluate(max_length=max_length)
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/trainer_seq2seq.py", line 159, in evaluate
+    return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/trainer.py", line 2972, in evaluate
+    output = eval_loop(
+             ^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/trainer.py", line 3161, in evaluation_loop
+    loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
+                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/trainer_seq2seq.py", line 282, in prediction_step
+    generated_tokens = self.model.generate(**inputs, **gen_kwargs)
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
+    return func(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/generation/utils.py", line 1402, in generate
+    self._validate_model_class()
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/generation/utils.py", line 1197, in _validate_model_class
+    raise TypeError(exception_message)
+TypeError: The current model class (BertModel) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'BertLMHeadModel'}
diff --git a/myerrors_1554.out b/myerrors_1554.out
new file mode 100644
index 0000000000000000000000000000000000000000..4c8a4883571c0064f7dd045ef0e867085b2f6fa5
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+++ b/myerrors_1554.out
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+
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+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.
+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [18,0,0], thread: [0,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [18,0,0], thread: [1,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [18,0,0], thread: [2,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [18,0,0], thread: [3,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [18,0,0], thread: [4,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [18,0,0], thread: [5,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [18,0,0], thread: [6,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [18,0,0], thread: [7,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [18,0,0], thread: [8,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [18,0,0], thread: [9,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [18,0,0], thread: [10,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [18,0,0], thread: [11,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [18,0,0], thread: [12,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
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+Traceback (most recent call last):
+  File "/mnt/storage/aatherton/hf_synth_trans/synth_translation.py", line 131, in <module>
+    trainer.evaluate(max_length=max_length)
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/trainer_seq2seq.py", line 159, in evaluate
+    return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/trainer.py", line 2972, in evaluate
+    output = eval_loop(
+             ^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/trainer.py", line 3161, in evaluation_loop
+    loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
+                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/trainer_seq2seq.py", line 282, in prediction_step
+    generated_tokens = self.model.generate(**inputs, **gen_kwargs)
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
+    return func(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/generation/utils.py", line 1596, in generate
+    return self.greedy_search(
+           ^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/generation/utils.py", line 2444, in greedy_search
+    outputs = self(
+              ^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py", line 1235, in forward
+    outputs = self.bert(
+              ^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py", line 1022, in forward
+    encoder_outputs = self.encoder(
+                      ^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py", line 612, in forward
+    layer_outputs = layer_module(
+                    ^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py", line 497, in forward
+    self_attention_outputs = self.attention(
+                             ^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py", line 427, in forward
+    self_outputs = self.self(
+                   ^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py", line 286, in forward
+    mixed_query_layer = self.query(hidden_states)
+                        ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/torch/nn/modules/linear.py", line 114, in forward
+    return F.linear(input, self.weight, self.bias)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+RuntimeError: CUDA error: CUBLAS_STATUS_NOT_INITIALIZED when calling `cublasCreate(handle)`
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+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [161,0,0], thread: [125,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [161,0,0], thread: [126,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
+/opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cuda/Indexing.cu:1146: indexSelectLargeIndex: block: [161,0,0], thread: [127,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
diff --git a/myerrors_1555.out b/myerrors_1555.out
new file mode 100644
index 0000000000000000000000000000000000000000..85e68c9fc635ed6872b05f9a4dbb9af44f37d515
--- /dev/null
+++ b/myerrors_1555.out
@@ -0,0 +1,46 @@
+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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+
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+Traceback (most recent call last):
+  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
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/requests/models.py", line 1021, in raise_for_status
+    raise HTTPError(http_error_msg, response=self)
+requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/aatherton2024/eng-nah-svo-cpt/resolve/main/config.json
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/utils/hub.py", line 428, in cached_file
+    resolved_file = hf_hub_download(
+                    ^^^^^^^^^^^^^^^^
+  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
+  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
+  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
+  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
+  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
+huggingface_hub.utils._errors.EntryNotFoundError: 404 Client Error. (Request ID: Root=1-650786ae-6603d136738db081161c4861;1f95ec5e-e41f-4227-9312-c914e4df78c5)
+
+Entry Not Found for url: https://huggingface.co/aatherton2024/eng-nah-svo-cpt/resolve/main/config.json.
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/mnt/storage/aatherton/hf_synth_trans/synth_translation.py", line 152, in <module>
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
+    config, kwargs = AutoConfig.from_pretrained(
+                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/models/auto/configuration_auto.py", line 1007, in from_pretrained
+    config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
+                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/configuration_utils.py", line 620, in get_config_dict
+    config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
+                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/configuration_utils.py", line 675, in _get_config_dict
+    resolved_config_file = cached_file(
+                           ^^^^^^^^^^^^
+  File "/mnt/storage/aatherton/anaconda3/envs/nmt/lib/python3.11/site-packages/transformers/utils/hub.py", line 479, in cached_file
+    raise EnvironmentError(
+OSError: aatherton2024/eng-nah-svo-cpt does not appear to have a file named config.json. Checkout 'https://huggingface.co/aatherton2024/eng-nah-svo-cpt/main' for available files.
diff --git a/myoutput_1551.out b/myoutput_1551.out
new file mode 100644
index 0000000000000000000000000000000000000000..922e54b747c5a72439d13ea889cb145b1f78b10a
--- /dev/null
+++ b/myoutput_1551.out
@@ -0,0 +1 @@
+evaluate1
diff --git a/myoutput_1552.out b/myoutput_1552.out
new file mode 100644
index 0000000000000000000000000000000000000000..922e54b747c5a72439d13ea889cb145b1f78b10a
--- /dev/null
+++ b/myoutput_1552.out
@@ -0,0 +1 @@
+evaluate1
diff --git a/myoutput_1553.out b/myoutput_1553.out
new file mode 100644
index 0000000000000000000000000000000000000000..922e54b747c5a72439d13ea889cb145b1f78b10a
--- /dev/null
+++ b/myoutput_1553.out
@@ -0,0 +1 @@
+evaluate1
diff --git a/myoutput_1554.out b/myoutput_1554.out
new file mode 100644
index 0000000000000000000000000000000000000000..922e54b747c5a72439d13ea889cb145b1f78b10a
--- /dev/null
+++ b/myoutput_1554.out
@@ -0,0 +1 @@
+evaluate1
diff --git a/myoutput_1555.out b/myoutput_1555.out
new file mode 100644
index 0000000000000000000000000000000000000000..1c5fb65ab4a88e7713dc7837f56a50a607d60f5b
--- /dev/null
+++ b/myoutput_1555.out
@@ -0,0 +1,5 @@
+evaluate1
+trainer train 1
+{'loss': 1.2688, 'learning_rate': 5.380116959064328e-06, 'epoch': 2.19}
+{'train_runtime': 56.3764, 'train_samples_per_second': 388.035, 'train_steps_per_second': 12.133, 'train_loss': 1.1421493842587833, 'epoch': 3.0}
+evaluate 2
diff --git a/run.sh b/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0cf9fbd94ebac03dc25a8049099b59a27e69bcf6
--- /dev/null
+++ b/run.sh
@@ -0,0 +1,9 @@
+#!/bin/sh
+#SBATCH -c 1
+#SBATCH -t 0-12:00
+#SBATCH -p dl
+#SBATCH --mem=10G
+#SBATCH -o myoutput_%j.out
+#SBATCH -e myerrors_%j.out
+#SBATCH --gres=gpu:1
+python synth_translation.py
\ No newline at end of file
diff --git a/synth_translation.py b/synth_translation.py
new file mode 100644
index 0000000000000000000000000000000000000000..71e2b1dfa953bf77deb3eebfe48bb8d233d78099
--- /dev/null
+++ b/synth_translation.py
@@ -0,0 +1,261 @@
+import transformers
+import numpy as np
+from datasets import load_dataset
+from transformers import AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer
+from transformers import DataCollatorForSeq2Seq
+import evaluate
+import numpy as np
+from transformers import Seq2SeqTrainingArguments
+from transformers import Seq2SeqTrainer
+from torch.utils.data import DataLoader
+from transformers import pipeline
+from transformers import AdamW
+from accelerate import Accelerator
+from transformers import get_scheduler
+from huggingface_hub import Repository, get_full_repo_name
+from tqdm.auto import tqdm
+import torch
+from torch import Tensor
+
+#load in dataset, setup tokenizer
+
+raw_datasets = load_dataset("aatherton2024/eng-nah-svo")
+model_checkpoint = "aatherton2024/eng-nah-svo-cpt"
+
+if False:
+    def get_training_corpus(raw_datasets):
+        return (
+            raw_datasets["train"][i : i + 1000]
+            for i in range(0, len(raw_datasets["train"]), 1000)
+        )
+
+    training_corpus = get_training_corpus(raw_datasets)
+    old_tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    tokenizer = old_tokenizer.train_new_from_iterator(training_corpus, 52000)
+
+    tokenizer.save_pretrained("eng-nah-svo-cpt")
+    tokenizer.push_to_hub("eng-nah-svo-cpt")
+
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+#contants
+max_length = 128
+
+#scan dataset, storing lists of english and french words then returning the tokenization of them
+def preprocess_function(examples):
+    inputs = examples["en"]
+    targets = examples["fr"]
+    model_inputs = tokenizer(
+        inputs, text_target=targets, max_length=max_length, truncation=True
+    )
+    return model_inputs
+
+#apply preprocessing in one go to all splits of the dataset
+tokenized_datasets = raw_datasets.map(
+    preprocess_function,
+    batched=True,
+    remove_columns=raw_datasets["train"].column_names,
+)
+
+# #model choice for this problem
+if False:
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+
+else:
+    from transformers import BertConfig, BertLMHeadModel
+    from transformers import AutoModel
+    # config = BertConfig(tokenizer.vocab_size, hidden_size=300,
+    #                     num_hidden_layers=2, num_attention_heads=2, is_decoder=True,
+    #                     add_cross_attention=True)
+    # model = BertLMHeadModel(config)
+    #model = AutoModel.from_pretrained("bert-base-cased")
+    model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-fr")
+
+#data collator takes tokenizer and the model to deal with padding for dynamic batching
+data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+
+#Using BLEU as our metric for this problem
+metric = evaluate.load("sacrebleu")
+
+#simple method to return test metrics
+def compute_metrics(eval_preds):
+    preds, labels = eval_preds
+    # In case the model returns more than the prediction logits
+    if isinstance(preds, tuple):
+        preds = preds[0]
+
+    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
+
+    # Replace -100s in the labels as we can't decode them
+    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+    # Some simple post-processing
+    decoded_preds = [pred.strip() for pred in decoded_preds]
+    decoded_labels = [[label.strip()] for label in decoded_labels]
+
+    result = metric.compute(predictions=decoded_preds, references=decoded_labels)
+    return {"bleu": result["score"]}
+
+### We now enter the fine-tuning phase of our model structure ###
+
+
+#definition of seq2seq training arguments --- figure what these are/use case
+args = Seq2SeqTrainingArguments(
+    f"eng-nah-svo-translation",
+    evaluation_strategy="no",
+    save_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=32,
+    per_device_eval_batch_size=64,
+    weight_decay=0.01,
+    save_total_limit=3,
+    num_train_epochs=3,
+    predict_with_generate=True,
+    fp16=False,
+    push_to_hub=True,
+)
+
+#pass all information to trainer
+trainer = Seq2SeqTrainer(
+    model,
+    args,
+    train_dataset=tokenized_datasets["train"],
+    eval_dataset=tokenized_datasets["test"],
+    data_collator=data_collator,
+    tokenizer=tokenizer,
+    compute_metrics=compute_metrics,
+)
+
+print("evaluate1")
+trainer.evaluate(max_length=max_length)
+print("trainer train 1")
+trainer.train()
+print("evaluate 2")
+trainer.evaluate(max_length=max_length)
+trainer.push_to_hub(tags="translation", commit_message="Training complete")
+
+
+
+tokenized_datasets.set_format("torch")
+train_dataloader = DataLoader(
+    tokenized_datasets["train"],
+    shuffle=True,
+    collate_fn=data_collator,
+    batch_size=8,
+)
+eval_dataloader = DataLoader(
+    tokenized_datasets["test"], collate_fn=data_collator, batch_size=8
+)
+
+model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+
+
+optimizer = AdamW(model.parameters(), lr=2e-5)
+
+
+
+accelerator = Accelerator()
+model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
+    model, optimizer, train_dataloader, eval_dataloader
+)
+
+
+
+num_train_epochs = 3
+num_update_steps_per_epoch = len(train_dataloader)
+num_training_steps = num_train_epochs * num_update_steps_per_epoch
+
+lr_scheduler = get_scheduler(
+    "linear",
+    optimizer=optimizer,
+    num_warmup_steps=0,
+    num_training_steps=num_training_steps,
+)
+
+
+
+model_name = "model"
+
+output_dir = "./output"
+repo = Repository("/mnt/storage/aatherton/hf_eng_fra_trans", clone_from="aatherton2024/hf_eng_fra_trans")
+
+
+def postprocess(predictions, labels):
+    predictions = predictions.cpu().numpy()
+    labels = labels.cpu().numpy()
+
+    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
+
+    # Replace -100 in the labels as we can't decode them.
+    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+    # Some simple post-processing
+    decoded_preds = [pred.strip() for pred in decoded_preds]
+    decoded_labels = [[label.strip()] for label in decoded_labels]
+    return decoded_preds, decoded_labels
+
+
+
+progress_bar = tqdm(range(num_training_steps))
+
+for epoch in range(num_train_epochs):
+    # Training
+    model.train()
+    for batch in train_dataloader:
+        outputs = model(**batch)
+        loss = outputs.loss
+        accelerator.backward(loss)
+
+        optimizer.step()
+        lr_scheduler.step()
+        optimizer.zero_grad()
+        progress_bar.update(1)
+
+    # Evaluation
+    model.eval()
+    for batch in tqdm(eval_dataloader):
+        with torch.no_grad():
+            generated_tokens = accelerator.unwrap_model(model).generate(
+                batch["input_ids"],
+                attention_mask=batch["attention_mask"],
+                max_length=128,
+            )
+        labels = batch["labels"]
+
+        # Necessary to pad predictions and labels for being gathered
+        generated_tokens = accelerator.pad_across_processes(
+            generated_tokens, dim=1, pad_index=tokenizer.pad_token_id
+        )
+        labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100)
+
+        predictions_gathered = accelerator.gather(generated_tokens)
+        labels_gathered = accelerator.gather(labels)
+
+        decoded_preds, decoded_labels = postprocess(predictions_gathered, labels_gathered)
+        metric.add_batch(predictions=decoded_preds, references=decoded_labels)
+
+    results = metric.compute()
+    print(f"epoch {epoch}, BLEU score: {results['score']:.2f}")
+
+    # Save and upload
+    accelerator.wait_for_everyone()
+    unwrapped_model = accelerator.unwrap_model(model)
+    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)
+    if accelerator.is_main_process:
+        tokenizer.save_pretrained(output_dir)
+        repo.push_to_hub(
+            commit_message=f"Training in progress epoch {epoch}", blocking=False
+        )
+
+
+
+# Replace this with your own checkpoint
+model_checkpoint = "aatherton2024/hf_eng_fra_reproduction"
+translator = pipeline("translation", model=model_checkpoint)
+translator("Default to expanded threads")
+translator(
+    "Unable to import %1 using the OFX importer plugin. This file is not the correct format."
+)
\ No newline at end of file