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https://git-lfs.github.com/spec/v1 -oid sha256:56d9bccc4236fa545061a943ba3e2f0625f218f8d5c0d42d828afd085f600e87 -size 1200792679 +oid sha256:f1d2f026869f78a1d66df11830fc0957b74e5642d73722f01e4ebcf696be5235 +size 2329728077 diff --git a/run_translation_t5_flax.py b/run_translation_t5_flax.py index 4baccf4510fb12a4fdfd9da281df103935bf20c5..4ddd600d61ba0a2e8960cef0ff46c531dd27b56f 100644 --- a/run_translation_t5_flax.py +++ b/run_translation_t5_flax.py @@ -376,7 +376,7 @@ def main(): if model_args.model_name_or_path: model = FlaxT5ForConditionalGeneration.from_pretrained( - model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) + model_args.model_name_or_path, config=config, seed=training_args.seed, from_pt=True, dtype=getattr(jnp, model_args.dtype) ) else: model = FlaxT5ForConditionalGeneration.from_config( @@ -430,15 +430,8 @@ def main(): # Setting padding="max_length" as we need fixed length inputs for jitted functions def preprocess_function(examples): - #Make it two way inputs = examples[text_column] - #inputs = examples[text_column][0:int(len(examples)/2)] - #inputs.extend(examples[summary_column][int(len(examples)/2):]) - targets = examples[summary_column] - #targets = examples[summary_column][0:int(len(examples)/2)] - #targets.extend(examples[text_column][int(len(examples)/2):]) - inputs = [prefix + inp for inp in inputs] model_inputs = tokenizer( inputs, max_length=data_args.max_source_length, padding="max_length", truncation=True, return_tensors="np" diff --git a/tokenizer_config.json b/tokenizer_config.json index 89428c3148e283768a103da8ae9453cce590f2ff..4c9b85c1eb481b40b74bbe43060e02c0e0983a02 100644 --- a/tokenizer_config.json +++ b/tokenizer_config.json @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ed4f9fa00c4248c8922aed5f185ad8c6a06e147807c44cf1998195f6dab6c2cb -size 412 +oid sha256:c49e34823066ecf3301423ba4abf79390bd22138eafa343e8c003cf7f64928a8 +size 408