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--- |
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license: apache-2.0 |
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base_model: google/flan-t5-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: flan-t5-base-nvidia |
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results: [] |
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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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# flan-t5-base-nvidia |
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This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) trained on [ajsbsd/datasets/nvidia-qa](https://huggingface.co/datasets/ajsbsd/nvidia-qa) |
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Imported from Kaggle (https://www.kaggle.com/datasets/gondimalladeepesh/nvidia-documentation-question-and-answer-pairs) |
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Q&A dataset for LLM finetuning about the NVIDIA about SDKs and blogs |
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This model is a fine-tuned version of google/flan-t5-small trained on |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7117 |
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- Rouge1: 0.4290 |
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- Rouge2: 0.2696 |
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- Rougel: 0.3880 |
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- Rougelsum: 0.3928 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 8 |
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- eval_batch_size: 4 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 2.4618 | 1.0 | 711 | 1.9707 | 0.3886 | 0.2185 | 0.3472 | 0.3522 | |
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| 2.0575 | 2.0 | 1422 | 1.8104 | 0.4066 | 0.2407 | 0.3647 | 0.3701 | |
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| 1.5839 | 3.0 | 2133 | 1.7351 | 0.4185 | 0.2558 | 0.3770 | 0.3821 | |
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| 1.4314 | 4.0 | 2844 | 1.7079 | 0.4252 | 0.2655 | 0.3840 | 0.3892 | |
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| 1.2582 | 5.0 | 3555 | 1.7117 | 0.4290 | 0.2696 | 0.3880 | 0.3928 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.7 |
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- Tokenizers 0.15.0 |
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