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Training complete

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README.md ADDED
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+ ---
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+ base_model: microsoft/graphcodebert-base
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+ tags:
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+ - Text2Text Generation
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+ - generated_from_trainer
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+ model-index:
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+ - name: graphCodeBert-finetuned-cXg-nl-to-code
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # graphCodeBert-finetuned-cXg-nl-to-code
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+
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+ This model is a fine-tuned version of [microsoft/graphcodebert-base](https://huggingface.co/microsoft/graphcodebert-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - eval_loss: 22.1633
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+ - eval_rouge1: 0.0772
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+ - eval_rouge2: 0.0056
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+ - eval_rougeL: 0.0553
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+ - eval_bleu: 0.6387
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+ - eval_meteor: 0.0767
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+ - eval_codebleu: 0.2058
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+ - eval_runtime: 41.4626
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+ - eval_samples_per_second: 0.241
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+ - eval_steps_per_second: 0.024
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+ - step: 0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 3
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+ - mixed_precision_training: Native AMP
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+
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+ ### Framework versions
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+
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+ - Transformers 4.42.4
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+ - Pytorch 2.3.1+cu121
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
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+ "architectures": [
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+ "RobertaForCausalLM"
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+ ],
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "initializer_range": 0.02,
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+ "is_decoder": true,
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+ "layer_norm_eps": 1e-05,
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+ "model_type": "roberta",
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+ "output_past": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.42.4",
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+ "use_cache": true,
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+ }
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