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test_trainer

Text Classification
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Generated from Trainer
text-embeddings-inference
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End of training

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  1. README.md +24 -13
  2. runs/Nov25_14-35-01_d63214356b26/events.out.tfevents.1732545308.d63214356b26.667.1 +2 -2
  3. runs/Nov25_14-35-01_d63214356b26/events.out.tfevents.1732546262.d63214356b26.667.2 +3 -0
README.md CHANGED
@@ -5,6 +5,9 @@ tags:
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  - generated_from_trainer
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  metrics:
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  - accuracy
 
 
 
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  model-index:
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  - name: test_trainer
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  results: []
@@ -17,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.2587
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- - Accuracy: 0.791
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- - Micro F1: 0.791
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- - Micro Precision: 0.791
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- - Micro Recall: 0.791
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  ## Model description
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@@ -41,20 +44,28 @@ More information needed
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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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 3
 
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro F1 | Micro Precision | Micro Recall |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|
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- | No log | 1.0 | 438 | 1.0074 | 0.775 | 0.775 | 0.775 | 0.775 |
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- | 0.1941 | 2.0 | 876 | 0.8466 | 0.789 | 0.789 | 0.789 | 0.789 |
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- | 0.1976 | 3.0 | 1314 | 1.2587 | 0.791 | 0.791 | 0.791 | 0.791 |
 
 
 
 
 
 
 
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  ### Framework versions
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
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+ - f1
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+ - precision
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+ - recall
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  model-index:
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  - name: test_trainer
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  results: []
 
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  This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2791
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+ - Accuracy: 0.794
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+ - F1: 0.7938
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+ - Precision: 0.7958
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+ - Recall: 0.7986
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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: 128
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+ - eval_batch_size: 64
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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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 | Precision | Recall |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 0.4814 | 1.0 | 55 | 0.5014 | 0.793 | 0.7925 | 0.7935 | 0.8008 |
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+ | 0.3957 | 2.0 | 110 | 0.5091 | 0.806 | 0.8050 | 0.8120 | 0.8030 |
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+ | 0.2667 | 3.0 | 165 | 0.6027 | 0.815 | 0.8149 | 0.8195 | 0.8148 |
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+ | 0.1823 | 4.0 | 220 | 0.7652 | 0.802 | 0.8015 | 0.8021 | 0.8088 |
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+ | 0.1114 | 5.0 | 275 | 0.8443 | 0.808 | 0.8080 | 0.8105 | 0.8117 |
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+ | 0.0862 | 6.0 | 330 | 0.9307 | 0.802 | 0.8021 | 0.8043 | 0.8072 |
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+ | 0.0422 | 7.0 | 385 | 1.0603 | 0.792 | 0.7919 | 0.7943 | 0.7958 |
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+ | 0.0323 | 8.0 | 440 | 1.1902 | 0.793 | 0.7928 | 0.7948 | 0.7982 |
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+ | 0.0195 | 9.0 | 495 | 1.2363 | 0.791 | 0.7909 | 0.7941 | 0.7941 |
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+ | 0.0172 | 10.0 | 550 | 1.2791 | 0.794 | 0.7938 | 0.7958 | 0.7986 |
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  ### Framework versions
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