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jq_emo_distilbert

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update model card README.md

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@@ -16,12 +16,12 @@ model-index:
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  name: emotion
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  type: emotion
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  config: split
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- split: validation[:1000]
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  args: split
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.575
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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
@@ -31,8 +31,8 @@ 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: 1.2677
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- - Accuracy: 0.575
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  ## Model description
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@@ -51,25 +51,34 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 2e-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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- - num_epochs: 2
 
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 63 | 1.4519 | 0.54 |
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- | No log | 2.0 | 126 | 1.2677 | 0.575 |
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - Transformers 4.26.1
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- - Pytorch 2.1.0.dev20230322
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  - Datasets 2.12.0
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  - Tokenizers 0.13.3
 
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  name: emotion
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  type: emotion
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  config: split
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+ split: validation
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  args: split
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.939
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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: 0.2100
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+ - Accuracy: 0.939
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 1e-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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+ - lr_scheduler_warmup_steps: 16000
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+ - num_epochs: 10
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 1.6731 | 1.0 | 1000 | 1.5737 | 0.4805 |
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+ | 1.224 | 2.0 | 2000 | 1.1049 | 0.5915 |
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+ | 0.8266 | 3.0 | 3000 | 0.7033 | 0.761 |
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+ | 0.4635 | 4.0 | 4000 | 0.3884 | 0.8845 |
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+ | 0.2832 | 5.0 | 5000 | 0.2466 | 0.9145 |
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+ | 0.1855 | 6.0 | 6000 | 0.2112 | 0.926 |
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+ | 0.1552 | 7.0 | 7000 | 0.1978 | 0.9285 |
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+ | 0.1426 | 8.0 | 8000 | 0.1937 | 0.9345 |
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+ | 0.1308 | 9.0 | 9000 | 0.2093 | 0.932 |
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+ | 0.1127 | 10.0 | 10000 | 0.2100 | 0.939 |
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  ### Framework versions
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  - Transformers 4.26.1
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+ - Pytorch 1.13.1+cu117
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  - Datasets 2.12.0
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  - Tokenizers 0.13.3