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RajuEEE
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RewardModel

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
text-embeddings-inference
Model card Files
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RajuEEE/RewardModel_2

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  1. README.md +7 -11
  2. config.json +1 -1
  3. pytorch_model.bin +1 -1
  4. training_args.bin +1 -1
README.md CHANGED
@@ -1,6 +1,6 @@
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  ---
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  license: apache-2.0
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- base_model: bert-base-uncased
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  tags:
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  - generated_from_trainer
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  metrics:
@@ -16,11 +16,11 @@ should probably proofread and complete it, then remove this comment. -->
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  # RewardModel
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- This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8815
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- - F1: 0.5086
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- - Roc Auc: 0.63
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  - Accuracy: 0.495
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  ## Model description
@@ -46,17 +46,13 @@ The following hyperparameters were used during training:
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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 | F1 | Roc Auc | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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- | 0.632 | 1.94 | 500 | 0.6207 | 0.0 | 0.5 | 0.0 |
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- | 0.5556 | 3.88 | 1000 | 0.6252 | 0.4703 | 0.6075 | 0.455 |
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- | 0.3224 | 5.81 | 1500 | 0.8815 | 0.5086 | 0.63 | 0.495 |
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- | 0.1332 | 7.75 | 2000 | 1.3508 | 0.4677 | 0.6 | 0.46 |
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- | 0.0555 | 9.69 | 2500 | 1.4896 | 0.48 | 0.61 | 0.48 |
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  ### Framework versions
 
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  ---
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  license: apache-2.0
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+ base_model: RajuEEE/RewardModel
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  tags:
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  - generated_from_trainer
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  metrics:
 
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  # RewardModel
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+ This model is a fine-tuned version of [RajuEEE/RewardModel](https://huggingface.co/RajuEEE/RewardModel) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.4180
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+ - F1: 0.4938
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+ - Roc Auc: 0.6200
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  - Accuracy: 0.495
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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: 2
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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+ | 0.1595 | 1.94 | 500 | 1.4180 | 0.4938 | 0.6200 | 0.495 |
 
 
 
 
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  ### Framework versions
config.json CHANGED
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  {
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- "_name_or_path": "bert-base-uncased",
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  "architectures": [
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  "BertForSequenceClassification"
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  ],
 
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  {
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+ "_name_or_path": "RajuEEE/RewardModel",
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  "architectures": [
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  "BertForSequenceClassification"
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  ],
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