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README.md
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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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- f1
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- accuracy
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model-index:
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- name:
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results:
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- task:
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name: Token Classification
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metrics:
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- name: Precision
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type: precision
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value:
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- name: Recall
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type: recall
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value:
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- name: F1
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type: f1
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value:
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- name: Accuracy
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type: accuracy
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value:
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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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#
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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:
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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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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size:
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- eval_batch_size:
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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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| 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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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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- f1
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- accuracy
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model-index:
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- name: Bert-NER
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results:
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- task:
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name: Token Classification
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metrics:
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- name: Precision
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type: precision
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value: 0.7254647322919372
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- name: Recall
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type: recall
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value: 0.8467001558981465
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- name: F1
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type: f1
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value: 0.7814079891293488
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- name: Accuracy
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type: accuracy
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value: 0.8557099199430039
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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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# Bert-NER
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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: 0.9081
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- Precision: 0.7255
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- Recall: 0.8467
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- F1: 0.7814
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- Accuracy: 0.8557
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## Model description
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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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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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.0934 | 1.0 | 501 | 0.6938 | 0.7190 | 0.8536 | 0.7805 | 0.8502 |
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| 0.035 | 2.0 | 1002 | 0.7709 | 0.7087 | 0.8383 | 0.7681 | 0.8446 |
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| 0.0196 | 3.0 | 1503 | 0.7814 | 0.7130 | 0.8439 | 0.7729 | 0.8477 |
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| 0.0109 | 4.0 | 2004 | 0.8572 | 0.7206 | 0.8467 | 0.7786 | 0.8526 |
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| 0.0065 | 5.0 | 2505 | 0.9081 | 0.7255 | 0.8467 | 0.7814 | 0.8557 |
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### Framework versions
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- Transformers 4.33.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.5
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- Tokenizers 0.13.3
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pytorch_model.bin
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