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vtiyyal1
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quality_model_apr3

Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
Inference Endpoints
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: distilbert-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: quality_model_apr3
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # quality_model_apr3
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+
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+ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0117
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+ - Mse: 0.0117
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 8
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+ - eval_batch_size: 8
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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: 3
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Mse |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 0.0209 | 0.05 | 50 | 0.0135 | 0.0135 |
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+ | 0.0179 | 0.11 | 100 | 0.0118 | 0.0118 |
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+ | 0.0153 | 0.16 | 150 | 0.0116 | 0.0116 |
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+ | 0.0159 | 0.22 | 200 | 0.0131 | 0.0131 |
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+ | 0.0169 | 0.27 | 250 | 0.0163 | 0.0163 |
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+ | 0.0116 | 0.32 | 300 | 0.0116 | 0.0116 |
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+ | 0.0094 | 0.38 | 350 | 0.0123 | 0.0123 |
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+ | 0.0168 | 0.43 | 400 | 0.0115 | 0.0115 |
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+ | 0.0224 | 0.48 | 450 | 0.0135 | 0.0135 |
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+ | 0.0144 | 0.54 | 500 | 0.0116 | 0.0116 |
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+ | 0.0147 | 0.59 | 550 | 0.0115 | 0.0115 |
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+ | 0.0117 | 0.65 | 600 | 0.0121 | 0.0121 |
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+ | 0.0198 | 0.7 | 650 | 0.0120 | 0.0120 |
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+ | 0.0119 | 0.75 | 700 | 0.0121 | 0.0121 |
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+ | 0.0166 | 0.81 | 750 | 0.0118 | 0.0118 |
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+ | 0.0096 | 0.86 | 800 | 0.0123 | 0.0123 |
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+ | 0.0166 | 0.92 | 850 | 0.0115 | 0.0115 |
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+ | 0.0181 | 0.97 | 900 | 0.0114 | 0.0114 |
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+ | 0.0128 | 1.02 | 950 | 0.0114 | 0.0114 |
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+ | 0.0174 | 1.08 | 1000 | 0.0113 | 0.0113 |
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+ | 0.0161 | 1.13 | 1050 | 0.0126 | 0.0126 |
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+ | 0.0174 | 1.19 | 1100 | 0.0141 | 0.0141 |
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+ | 0.016 | 1.24 | 1150 | 0.0114 | 0.0114 |
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+ | 0.0098 | 1.29 | 1200 | 0.0114 | 0.0114 |
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+ | 0.0179 | 1.35 | 1250 | 0.0126 | 0.0126 |
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+ | 0.0141 | 1.4 | 1300 | 0.0115 | 0.0115 |
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+ | 0.0118 | 1.45 | 1350 | 0.0116 | 0.0116 |
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+ | 0.0115 | 1.51 | 1400 | 0.0113 | 0.0113 |
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+ | 0.0118 | 1.56 | 1450 | 0.0113 | 0.0113 |
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+ | 0.0165 | 1.62 | 1500 | 0.0118 | 0.0118 |
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+ | 0.0129 | 1.67 | 1550 | 0.0113 | 0.0113 |
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+ | 0.011 | 1.72 | 1600 | 0.0118 | 0.0118 |
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+ | 0.0128 | 1.78 | 1650 | 0.0120 | 0.0120 |
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+ | 0.0145 | 1.83 | 1700 | 0.0124 | 0.0124 |
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+ | 0.014 | 1.89 | 1750 | 0.0114 | 0.0114 |
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+ | 0.0155 | 1.94 | 1800 | 0.0114 | 0.0114 |
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+ | 0.0144 | 1.99 | 1850 | 0.0114 | 0.0114 |
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+ | 0.0141 | 2.05 | 1900 | 0.0114 | 0.0114 |
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+ | 0.0108 | 2.1 | 1950 | 0.0117 | 0.0117 |
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+ | 0.0109 | 2.16 | 2000 | 0.0113 | 0.0113 |
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+ | 0.0124 | 2.21 | 2050 | 0.0132 | 0.0132 |
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+ | 0.0169 | 2.26 | 2100 | 0.0123 | 0.0123 |
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+ | 0.0115 | 2.32 | 2150 | 0.0120 | 0.0120 |
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+ | 0.0102 | 2.37 | 2200 | 0.0117 | 0.0117 |
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+ | 0.0189 | 2.42 | 2250 | 0.0116 | 0.0116 |
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+ | 0.0136 | 2.48 | 2300 | 0.0115 | 0.0115 |
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+ | 0.0116 | 2.53 | 2350 | 0.0119 | 0.0119 |
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+ | 0.0141 | 2.59 | 2400 | 0.0119 | 0.0119 |
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+ | 0.0098 | 2.64 | 2450 | 0.0120 | 0.0120 |
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+ | 0.0081 | 2.69 | 2500 | 0.0117 | 0.0117 |
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+ | 0.009 | 2.75 | 2550 | 0.0119 | 0.0119 |
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+ | 0.0121 | 2.8 | 2600 | 0.0118 | 0.0118 |
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+ | 0.0128 | 2.86 | 2650 | 0.0123 | 0.0123 |
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+ | 0.0131 | 2.91 | 2700 | 0.0117 | 0.0117 |
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+ | 0.009 | 2.96 | 2750 | 0.0117 | 0.0117 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.3
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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