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experiment_labels_roberta_base

Text Classification
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roberta
Generated from Trainer
text-embeddings-inference
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tkbarb10 commited on
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Roberta base model with correct label mapping

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Files changed (3) hide show
  1. README.md +8 -8
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -18,10 +18,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5795
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- - Accuracy: 0.7763
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- - F1 Macro: 0.7579
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- - F1 Weighted: 0.7761
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  ## Model description
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@@ -46,7 +46,7 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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_steps: 200
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  - num_epochs: 2
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  - mixed_precision_training: Native AMP
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@@ -54,13 +54,13 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|
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- | 0.6400 | 1.0 | 628 | 0.6059 | 0.7663 | 0.7459 | 0.7682 |
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- | 0.5020 | 2.0 | 1256 | 0.5795 | 0.7763 | 0.7579 | 0.7761 |
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  ### Framework versions
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  - Transformers 5.0.0
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- - Pytorch 2.9.0+cu128
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  - Datasets 4.0.0
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  - Tokenizers 0.22.2
 
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  This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6205
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+ - Accuracy: 0.7567
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+ - F1 Macro: 0.7431
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+ - F1 Weighted: 0.7572
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  ## Model description
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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_steps: 300
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  - num_epochs: 2
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  - mixed_precision_training: Native AMP
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|
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+ | 0.6546 | 1.0 | 1540 | 0.6516 | 0.7371 | 0.7232 | 0.7382 |
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+ | 0.5131 | 2.0 | 3080 | 0.6205 | 0.7567 | 0.7431 | 0.7572 |
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  ### Framework versions
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  - Transformers 5.0.0
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+ - Pytorch 2.10.0+cu128
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  - Datasets 4.0.0
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  - Tokenizers 0.22.2
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