End of training
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README.md
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metrics:
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- name: Precision
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type: precision
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value: 0
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- name: Recall
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type: recall
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value: 0.
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- name: F1
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type: f1
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value: 0.
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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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Precision: 0
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- Recall:
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- F1:
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- Accuracy:
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## Model description
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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:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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### Framework versions
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metrics:
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- name: Precision
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type: precision
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value: 1.0
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- name: Recall
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type: recall
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value: 0.999957470335559
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- name: F1
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type: f1
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value: 0.9999787347155767
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- name: Accuracy
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type: accuracy
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value: 0.9999890011988694
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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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0000
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- Precision: 1.0
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- Recall: 1.0000
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- F1: 1.0000
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- Accuracy: 1.0000
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## Model description
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.0127 | 1.0 | 626 | 0.0069 | 0.9955 | 0.9957 | 0.9956 | 0.9986 |
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| 0.01 | 2.0 | 1252 | 0.0068 | 0.9972 | 0.9971 | 0.9972 | 0.9991 |
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| 0.0075 | 3.0 | 1878 | 0.0029 | 0.9987 | 0.9982 | 0.9984 | 0.9995 |
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| 0.006 | 4.0 | 2504 | 0.0010 | 0.9994 | 0.9994 | 0.9994 | 0.9998 |
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| 0.0052 | 5.0 | 3130 | 0.0007 | 0.9997 | 0.9997 | 0.9997 | 0.9999 |
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| 0.0032 | 6.0 | 3756 | 0.0003 | 0.9999 | 0.9998 | 0.9999 | 1.0000 |
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| 0.003 | 7.0 | 4382 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
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| 0.0013 | 8.0 | 5008 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
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| 0.0013 | 9.0 | 5634 | 0.0001 | 1.0000 | 0.9999 | 0.9999 | 1.0000 |
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| 0.0011 | 10.0 | 6260 | 0.0000 | 1.0 | 1.0000 | 1.0000 | 1.0000 |
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### Framework versions
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