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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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datasets: |
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- mtc/span_absinth_with_articles_german_faithfulness_detection_dataset |
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model-index: |
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- name: google-flan-t5-base_MAX-CONTEXT-LEN-1024_MAX-GEN-LEN-256_span_absinth_faithfulness_multi_label_classification_sugary-trellis-2024-07-15 |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/background-tool/span_absinth_evaluation/runs/1e5w4pqx) |
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# google-flan-t5-base_MAX-CONTEXT-LEN-1024_MAX-GEN-LEN-256_span_absinth_faithfulness_multi_label_classification_sugary-trellis-2024-07-15 |
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This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the mtc/span_absinth_with_articles_german_faithfulness_detection_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1605 |
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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: 4 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 0.9007 | 0.1534 | 100 | 0.5516 | |
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| 0.2893 | 0.3067 | 200 | 0.2852 | |
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| 0.2428 | 0.4601 | 300 | 0.2061 | |
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| 0.2088 | 0.6135 | 400 | 0.1619 | |
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| 0.1627 | 0.7669 | 500 | 0.1604 | |
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| 0.1662 | 0.9202 | 600 | 0.1678 | |
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| 0.1602 | 1.0736 | 700 | 0.1578 | |
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| 0.1219 | 1.2270 | 800 | 0.1594 | |
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| 0.1135 | 1.3804 | 900 | 0.1677 | |
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| 0.1024 | 1.5337 | 1000 | 0.1620 | |
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| 0.111 | 1.6871 | 1100 | 0.1586 | |
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| 0.1053 | 1.8405 | 1200 | 0.1542 | |
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| 0.0922 | 1.9939 | 1300 | 0.1526 | |
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| 0.087 | 2.1472 | 1400 | 0.1585 | |
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| 0.1259 | 2.3006 | 1500 | 0.1552 | |
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| 0.1027 | 2.4540 | 1600 | 0.1584 | |
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| 0.0771 | 2.6074 | 1700 | 0.1591 | |
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| 0.0918 | 2.7607 | 1800 | 0.1609 | |
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| 0.0782 | 2.9141 | 1900 | 0.1604 | |
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### Framework versions |
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- Transformers 4.42.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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