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distilbert-base-uncased-finetuned-emotion

Text Classification
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PyTorch
distilbert
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  1. README.md +16 -11
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -23,10 +23,10 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.935
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  - name: F1
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  type: f1
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- value: 0.9352944011245874
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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
@@ -36,9 +36,9 @@ 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 emotion dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1576
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- - Accuracy: 0.935
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- - F1: 0.9353
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  ## Model description
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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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- - num_epochs: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | 1.0414 | 1.0 | 125 | 0.4652 | 0.873 | 0.8638 |
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- | 0.3107 | 2.0 | 250 | 0.2140 | 0.925 | 0.9248 |
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- | 0.1782 | 3.0 | 375 | 0.1756 | 0.9375 | 0.9377 |
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- | 0.139 | 4.0 | 500 | 0.1633 | 0.9375 | 0.9375 |
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- | 0.1188 | 5.0 | 625 | 0.1576 | 0.935 | 0.9353 |
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9405
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  - name: F1
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  type: f1
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+ value: 0.9405428930790032
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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 emotion dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2602
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+ - Accuracy: 0.9405
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+ - F1: 0.9405
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  ## Model description
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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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+ - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | 0.04 | 1.0 | 125 | 0.2096 | 0.9385 | 0.9386 |
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+ | 0.041 | 2.0 | 250 | 0.2138 | 0.9395 | 0.9396 |
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+ | 0.0323 | 3.0 | 375 | 0.2165 | 0.94 | 0.9401 |
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+ | 0.024 | 4.0 | 500 | 0.2315 | 0.941 | 0.9412 |
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+ | 0.0229 | 5.0 | 625 | 0.2263 | 0.9375 | 0.9374 |
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+ | 0.0179 | 6.0 | 750 | 0.2561 | 0.9415 | 0.9418 |
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+ | 0.0149 | 7.0 | 875 | 0.2518 | 0.943 | 0.9433 |
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+ | 0.0144 | 8.0 | 1000 | 0.2574 | 0.941 | 0.9409 |
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+ | 0.011 | 9.0 | 1125 | 0.2598 | 0.943 | 0.9430 |
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+ | 0.009 | 10.0 | 1250 | 0.2602 | 0.9405 | 0.9405 |
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  ### Framework versions
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