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ft-distilbert-base-uncased-with-wnut17-v-0

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ft-distilbert-base-uncased-with-wnut17-v-0

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Files changed (3) hide show
  1. README.md +11 -11
  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: 0.5096359743040685
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  - name: Recall
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  type: recall
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- value: 0.2205746061167748
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  - name: F1
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  type: f1
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- value: 0.3078913324708926
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  - name: Accuracy
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  type: accuracy
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- value: 0.9379248428882904
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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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2920
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- - Precision: 0.5096
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- - Recall: 0.2206
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- - F1: 0.3079
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- - Accuracy: 0.9379
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  ## Model description
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@@ -79,8 +79,8 @@ 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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- | No log | 1.0 | 107 | 0.3043 | 0.4852 | 0.1520 | 0.2315 | 0.9338 |
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- | No log | 2.0 | 214 | 0.2920 | 0.5096 | 0.2206 | 0.3079 | 0.9379 |
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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: 0.4975845410628019
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  - name: Recall
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  type: recall
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+ value: 0.19091751621872105
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  - name: F1
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  type: f1
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+ value: 0.27595445411922304
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9358300200931983
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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 wnut_17 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2988
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+ - Precision: 0.4976
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+ - Recall: 0.1909
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+ - F1: 0.2760
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+ - Accuracy: 0.9358
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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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+ | No log | 1.0 | 107 | 0.3079 | 0.3886 | 0.0630 | 0.1085 | 0.9301 |
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+ | No log | 2.0 | 214 | 0.2988 | 0.4976 | 0.1909 | 0.2760 | 0.9358 |
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  ### Framework versions
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