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--- |
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license: apache-2.0 |
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base_model: google/flan-t5-small |
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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-small-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-small-nvidia |
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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](https://huggingface.co/google/flan-t5-small) trained on [ajsbsd/datasets/nvidia-qa](https://huggingface.co/datasets/ajsbsd/nvidia-qa) |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0857 |
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- Rouge1: 0.3970 |
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- Rouge2: 0.2295 |
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- Rougel: 0.3537 |
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- Rougelsum: 0.3593 |
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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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| 2.8569 | 1.0 | 711 | 2.3454 | 0.3748 | 0.2036 | 0.3321 | 0.3375 | |
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| 2.5034 | 2.0 | 1422 | 2.2079 | 0.3841 | 0.2143 | 0.3417 | 0.3465 | |
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| 2.1886 | 3.0 | 2133 | 2.1342 | 0.3900 | 0.2227 | 0.3494 | 0.3543 | |
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| 2.0784 | 4.0 | 2844 | 2.0972 | 0.3951 | 0.2267 | 0.3522 | 0.3571 | |
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| 1.9843 | 5.0 | 3555 | 2.0857 | 0.3970 | 0.2295 | 0.3537 | 0.3593 | |
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### Framework versions |
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- Transformers 4.35.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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