PabloGuinea
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update model card README.md
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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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@@ -41,13 +41,13 @@ should probably proofread and complete it, then remove this comment. -->
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# bert-base-uncased-finetuned-ner
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This model is a fine-tuned version of [
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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: 0.
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- F1: 0.
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- Accuracy: 0.
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## Model description
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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: 0.9290076335877863
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- name: Recall
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type: recall
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value: 0.9391467313416382
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- name: F1
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type: f1
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value: 0.9340496683295871
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- name: Accuracy
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type: accuracy
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value: 0.9849767124843803
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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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# bert-base-uncased-finetuned-ner
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0664
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- Precision: 0.9290
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- Recall: 0.9391
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- F1: 0.9340
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- Accuracy: 0.9850
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.1694 | 1.0 | 878 | 0.0677 | 0.9162 | 0.9141 | 0.9152 | 0.9812 |
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| 0.0571 | 2.0 | 1756 | 0.0657 | 0.9193 | 0.9286 | 0.9239 | 0.9832 |
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| 0.0283 | 3.0 | 2634 | 0.0664 | 0.9290 | 0.9391 | 0.9340 | 0.9850 |
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### Framework versions
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