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README.md
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---
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license: apache-2.0
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base_model: distilbert-base-uncased
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tags:
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- generated_from_trainer
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datasets:
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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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# my_awesome_wnut_model
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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: 0.9995
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- Recall: 0.
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- F1: 0.9995
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- Accuracy: 0.9997
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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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- Transformers 4.
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- Pytorch 2.0.1+cu118
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- Datasets 2.
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- Tokenizers 0.13.3
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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metrics:
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- name: Precision
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type: precision
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value: 0.999537251272559
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- name: Recall
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type: recall
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value: 0.999537251272559
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- name: F1
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type: f1
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value: 0.999537251272559
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- name: Accuracy
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type: accuracy
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value: 0.9997335485246202
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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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# my_awesome_wnut_model
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ner dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0003
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- Precision: 0.9995
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- Recall: 0.9995
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- F1: 0.9995
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- Accuracy: 0.9997
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.0364 | 1.0 | 688 | 0.0026 | 0.9964 | 0.9965 | 0.9964 | 0.9979 |
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| 0.0088 | 2.0 | 1376 | 0.0008 | 0.9991 | 0.9988 | 0.9990 | 0.9994 |
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| 0.0017 | 3.0 | 2064 | 0.0003 | 0.9995 | 0.9995 | 0.9995 | 0.9997 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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