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bert
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text-embeddings-inference
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avkumararun/bert-tiny

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  1. README.md +57 -12
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -21,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0985
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- - Accuracy: 0.91
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- - F1: 0.9096
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- - Precision: 0.9139
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- - Recall: 0.9123
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  ## Model description
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@@ -50,22 +50,67 @@ 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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- - num_epochs: 5
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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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- | No log | 1.0 | 125 | 1.5109 | 0.59 | 0.6026 | 0.7073 | 0.5895 |
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- | No log | 2.0 | 250 | 1.3482 | 0.828 | 0.8293 | 0.8313 | 0.8301 |
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- | No log | 3.0 | 375 | 1.2057 | 0.882 | 0.8806 | 0.8837 | 0.8850 |
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- | 1.4080 | 4.0 | 500 | 1.1231 | 0.906 | 0.9056 | 0.9099 | 0.9085 |
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- | 1.4080 | 5.0 | 625 | 1.0985 | 0.91 | 0.9096 | 0.9139 | 0.9123 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - Transformers 5.0.0
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- - Pytorch 2.9.0+cpu
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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 [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0081
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+ - Accuracy: 1.0
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+ - F1: 1.0
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+ - Precision: 1.0
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+ - Recall: 1.0
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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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+ - num_epochs: 50
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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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+ | No log | 1.0 | 125 | 1.4595 | 0.666 | 0.5919 | 0.7955 | 0.6412 |
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+ | No log | 2.0 | 250 | 1.2117 | 0.894 | 0.8814 | 0.8937 | 0.8839 |
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+ | No log | 3.0 | 375 | 0.9703 | 0.924 | 0.9164 | 0.9352 | 0.9149 |
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+ | 1.2705 | 4.0 | 500 | 0.7647 | 0.934 | 0.9284 | 0.9428 | 0.9262 |
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+ | 1.2705 | 5.0 | 625 | 0.5898 | 0.97 | 0.9664 | 0.9722 | 0.9659 |
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+ | 1.2705 | 6.0 | 750 | 0.4600 | 0.97 | 0.9664 | 0.9722 | 0.9659 |
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+ | 1.2705 | 7.0 | 875 | 0.3596 | 0.97 | 0.9664 | 0.9722 | 0.9659 |
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+ | 0.5486 | 8.0 | 1000 | 0.2753 | 0.97 | 0.9664 | 0.9722 | 0.9659 |
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+ | 0.5486 | 9.0 | 1125 | 0.1988 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.5486 | 10.0 | 1250 | 0.1469 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.5486 | 11.0 | 1375 | 0.1139 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.1935 | 12.0 | 1500 | 0.0904 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.1935 | 13.0 | 1625 | 0.0743 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.1935 | 14.0 | 1750 | 0.0630 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.1935 | 15.0 | 1875 | 0.0542 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0781 | 16.0 | 2000 | 0.0473 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0781 | 17.0 | 2125 | 0.0418 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0781 | 18.0 | 2250 | 0.0374 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0781 | 19.0 | 2375 | 0.0337 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0449 | 20.0 | 2500 | 0.0305 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0449 | 21.0 | 2625 | 0.0279 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0449 | 22.0 | 2750 | 0.0256 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0449 | 23.0 | 2875 | 0.0236 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0305 | 24.0 | 3000 | 0.0219 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0305 | 25.0 | 3125 | 0.0204 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0305 | 26.0 | 3250 | 0.0190 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0305 | 27.0 | 3375 | 0.0178 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0224 | 28.0 | 3500 | 0.0167 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0224 | 29.0 | 3625 | 0.0157 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0224 | 30.0 | 3750 | 0.0149 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0224 | 31.0 | 3875 | 0.0141 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0181 | 32.0 | 4000 | 0.0134 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0181 | 33.0 | 4125 | 0.0127 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0181 | 34.0 | 4250 | 0.0121 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0181 | 35.0 | 4375 | 0.0116 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0141 | 36.0 | 4500 | 0.0111 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0141 | 37.0 | 4625 | 0.0107 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0141 | 38.0 | 4750 | 0.0103 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0141 | 39.0 | 4875 | 0.0099 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0120 | 40.0 | 5000 | 0.0096 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0120 | 41.0 | 5125 | 0.0093 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0120 | 42.0 | 5250 | 0.0091 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0120 | 43.0 | 5375 | 0.0088 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0108 | 44.0 | 5500 | 0.0087 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0108 | 45.0 | 5625 | 0.0085 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0108 | 46.0 | 5750 | 0.0083 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0108 | 47.0 | 5875 | 0.0082 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0096 | 48.0 | 6000 | 0.0082 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0096 | 49.0 | 6125 | 0.0081 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0096 | 50.0 | 6250 | 0.0081 | 1.0 | 1.0 | 1.0 | 1.0 |
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  ### Framework versions
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  - Transformers 5.0.0
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+ - Pytorch 2.9.0+cu126
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  - Datasets 4.0.0
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  - Tokenizers 0.22.2
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