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--- |
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license: apache-2.0 |
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base_model: gsarti/it5-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- geopolitica |
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metrics: |
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- rouge |
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model-index: |
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- name: my_awesome_geopolitical_model |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: geopolitica |
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type: geopolitica |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.1409 |
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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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# my_awesome_geopolitical_model |
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This model is a fine-tuned version of [gsarti/it5-small](https://huggingface.co/gsarti/it5-small) on the geopolitica dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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- Rouge1: 0.1409 |
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- Rouge2: 0.0203 |
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- Rougel: 0.1247 |
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- Rougelsum: 0.125 |
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- Gen Len: 18.781 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| No log | 1.0 | 53 | nan | 0.1409 | 0.0203 | 0.1247 | 0.125 | 18.781 | |
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| No log | 2.0 | 106 | nan | 0.1409 | 0.0203 | 0.1247 | 0.125 | 18.781 | |
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| No log | 3.0 | 159 | nan | 0.1409 | 0.0203 | 0.1247 | 0.125 | 18.781 | |
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| No log | 4.0 | 212 | nan | 0.1409 | 0.0203 | 0.1247 | 0.125 | 18.781 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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