Theoreticallyhugo
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trainer: training complete at 2024-02-17 18:59:08.293102.
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README.md
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.
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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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This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the fancy_dataset dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1948
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- : {'precision': 0.
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- O: {'precision': 0.9301474791357037, 'recall': 0.8771967654986523, 'f1-score': 0.9028964598823661, 'support': 9275.0}
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- Accuracy: 0.
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- Macro avg: {'precision': 0.
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- Weighted avg: {'precision': 0.
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## Model description
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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| No log | 1.0 | 41 | 0.2901 | {'precision': 0.
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| No log | 2.0 | 82 | 0.2109 | {'precision': 0.
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| No log | 3.0 | 123 | 0.1948 | {'precision': 0.
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### Framework versions
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- Transformers 4.37.
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- Pytorch 2.
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- Datasets 2.
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- Tokenizers 0.15.
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9297359274785911
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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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This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the fancy_dataset dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1948
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- B: {'precision': 0.773955773955774, 'recall': 0.8923512747875354, 'f1-score': 0.8289473684210527, 'support': 1059.0}
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- I: {'precision': 0.9401371161027814, 'recall': 0.9597155049786629, 'f1-score': 0.949825430791756, 'support': 17575.0}
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- O: {'precision': 0.9301474791357037, 'recall': 0.8771967654986523, 'f1-score': 0.9028964598823661, 'support': 9275.0}
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- Accuracy: 0.9297
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- Macro avg: {'precision': 0.8814134563980863, 'recall': 0.9097545150882835, 'f1-score': 0.893889753031725, 'support': 27909.0}
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- Weighted avg: {'precision': 0.9305115500057043, 'recall': 0.9297359274785911, 'f1-score': 0.929642834739043, 'support': 27909.0}
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## Model description
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | B | I | O | Accuracy | Macro avg | Weighted avg |
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|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
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| No log | 1.0 | 41 | 0.2901 | {'precision': 0.8336673346693386, 'recall': 0.392823418319169, 'f1-score': 0.5340179717586649, 'support': 1059.0} | {'precision': 0.9134376209164778, 'recall': 0.9402560455192034, 'f1-score': 0.9266528346324231, 'support': 17575.0} | {'precision': 0.8671531280180277, 'recall': 0.871266846361186, 'f1-score': 0.8692051199311606, 'support': 9275.0} | 0.8966 | {'precision': 0.8714193612012814, 'recall': 0.7347821033998527, 'f1-score': 0.7766253087740829, 'support': 27909.0} | {'precision': 0.8950290285352085, 'recall': 0.8965566663083593, 'f1-score': 0.892662800104582, 'support': 27909.0} |
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| No log | 2.0 | 82 | 0.2109 | {'precision': 0.7553191489361702, 'recall': 0.8715769593956563, 'f1-score': 0.8092941692240245, 'support': 1059.0} | {'precision': 0.9298303409652446, 'recall': 0.9635846372688478, 'f1-score': 0.9464066167430425, 'support': 17575.0} | {'precision': 0.9366296908189757, 'recall': 0.8557412398921833, 'f1-score': 0.8943602456476422, 'support': 9275.0} | 0.9243 | {'precision': 0.8739263935734636, 'recall': 0.8969676121855624, 'f1-score': 0.883353677204903, 'support': 27909.0} | {'precision': 0.9254681860164671, 'recall': 0.9242538249310258, 'f1-score': 0.9239073450445768, 'support': 27909.0} |
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| No log | 3.0 | 123 | 0.1948 | {'precision': 0.773955773955774, 'recall': 0.8923512747875354, 'f1-score': 0.8289473684210527, 'support': 1059.0} | {'precision': 0.9401371161027814, 'recall': 0.9597155049786629, 'f1-score': 0.949825430791756, 'support': 17575.0} | {'precision': 0.9301474791357037, 'recall': 0.8771967654986523, 'f1-score': 0.9028964598823661, 'support': 9275.0} | 0.9297 | {'precision': 0.8814134563980863, 'recall': 0.9097545150882835, 'f1-score': 0.893889753031725, 'support': 27909.0} | {'precision': 0.9305115500057043, 'recall': 0.9297359274785911, 'f1-score': 0.929642834739043, 'support': 27909.0} |
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### Framework versions
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- Transformers 4.37.2
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- Pytorch 2.2.0+cu121
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- Datasets 2.17.0
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- Tokenizers 0.15.2
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