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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 commited on
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RajuEEE/RewardModel

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Files changed (4) hide show
  1. README.md +8 -8
  2. config.json +1 -1
  3. pytorch_model.bin +1 -1
  4. 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 [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: 0.6265
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- - F1: 0.0207
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- - Roc Auc: 0.5019
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- - Accuracy: 0.0106
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  ## Model description
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@@ -50,14 +50,14 @@ The following hyperparameters were used during training:
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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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- | No log | 1.0 | 258 | 0.6265 | 0.0207 | 0.5019 | 0.0106 |
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  ### Framework versions
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- - Transformers 4.32.0
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  - Pytorch 2.0.1+cu118
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  - Datasets 2.14.4
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  - Tokenizers 0.13.3
 
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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: 0.6338
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+ - F1: 0.0
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+ - Roc Auc: 0.5
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+ - Accuracy: 0.0
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  ## Model description
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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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+ | No log | 1.0 | 258 | 0.6338 | 0.0 | 0.5 | 0.0 |
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  ### Framework versions
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+ - Transformers 4.32.1
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  - Pytorch 2.0.1+cu118
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  - Datasets 2.14.4
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  - Tokenizers 0.13.3
config.json CHANGED
@@ -30,7 +30,7 @@
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  "position_embedding_type": "absolute",
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  "problem_type": "multi_label_classification",
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  "torch_dtype": "float32",
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- "transformers_version": "4.32.0",
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  "type_vocab_size": 2,
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  "use_cache": true,
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  "vocab_size": 30522
 
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  "position_embedding_type": "absolute",
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  "problem_type": "multi_label_classification",
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  "torch_dtype": "float32",
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+ "transformers_version": "4.32.1",
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  "type_vocab_size": 2,
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  "use_cache": true,
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  "vocab_size": 30522
pytorch_model.bin CHANGED
@@ -1,3 +1,3 @@
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training_args.bin CHANGED
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