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mdroth
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bert-finetuned-ner

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  1. README.md +10 -10
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.7833402370948971
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  - name: Recall
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  type: recall
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  value: 0.8147760612215589
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  - name: F1
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  type: f1
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- value: 0.798748969206943
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  - name: Accuracy
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  type: accuracy
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- value: 0.9425415670481714
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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
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the few-nerd dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2154
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- - Precision: 0.7833
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  - Recall: 0.8148
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- - F1: 0.7987
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- - Accuracy: 0.9425
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  ## Model description
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@@ -79,9 +79,9 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.1957 | 1.0 | 32942 | 0.1963 | 0.7533 | 0.7992 | 0.7756 | 0.9386 |
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- | 0.1596 | 2.0 | 65884 | 0.2025 | 0.7768 | 0.8063 | 0.7913 | 0.9416 |
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- | 0.1207 | 3.0 | 98826 | 0.2154 | 0.7833 | 0.8148 | 0.7987 | 0.9425 |
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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: 0.7844853130000198
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  - name: Recall
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  type: recall
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  value: 0.8147760612215589
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  - name: F1
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  type: f1
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+ value: 0.799343826738054
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9428779215112315
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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 [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the few-nerd dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2164
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+ - Precision: 0.7845
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  - Recall: 0.8148
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+ - F1: 0.7993
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+ - Accuracy: 0.9429
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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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+ | 0.1953 | 1.0 | 32942 | 0.1933 | 0.7670 | 0.7968 | 0.7816 | 0.9395 |
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+ | 0.1573 | 2.0 | 65884 | 0.2051 | 0.7850 | 0.8034 | 0.7941 | 0.9416 |
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+ | 0.1256 | 3.0 | 98826 | 0.2164 | 0.7845 | 0.8148 | 0.7993 | 0.9429 |
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  ### Framework versions
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