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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- gsm8k |
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model-index: |
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- name: flan-t5-large-finetuned-gsm8k |
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results: [] |
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widget: |
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- text: "Please, answer the following question reasoning step-by-step: |
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If Manu eats twice a day, how many meals does he take for a week?" |
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- text: "Please, answer the following question reasoning step-by-step: Manu bought 4 apples and lost one in the market. How many apples does Manu have?" |
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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-large-finetuned-gsm8k |
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This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co./google/flan-t5-large) on the gsm8k dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3091 |
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- Rouge2 Precision: 0.4454 |
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- Rouge2 Recall: 0.0953 |
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- Rouge2 Fmeasure: 0.152 |
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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: 5e-05 |
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- train_batch_size: 2 |
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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: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |
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|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| |
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| 0.34 | 1.0 | 3737 | 0.3206 | 0.4241 | 0.089 | 0.1423 | |
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| 0.2786 | 2.0 | 7474 | 0.3089 | 0.4334 | 0.0916 | 0.1463 | |
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| 0.247 | 3.0 | 11211 | 0.3074 | 0.4461 | 0.095 | 0.1515 | |
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| 0.2283 | 4.0 | 14948 | 0.3091 | 0.4454 | 0.0953 | 0.152 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.2 |
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