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t5-base_cola_dense

Text Classification
Transformers
PyTorch
t5
Generated from Trainer
Eval Results
text-generation-inference
Inference Endpoints
Model card Files Community
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  1. README.md +92 -8
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@@ -5,9 +5,24 @@ tags:
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  - generated_from_trainer
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  datasets:
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  - glue
 
 
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  model-index:
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  - name: t5-base_cola_dense
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- results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
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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
@@ -17,12 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the glue dataset.
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  It achieves the following results on the evaluation set:
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- - eval_loss: 0.6850
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- - eval_accuracy: 0.6088
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- - eval_runtime: 1.6155
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- - eval_samples_per_second: 645.626
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- - eval_steps_per_second: 10.523
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- - step: 0
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  ## Model description
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@@ -50,7 +61,80 @@ The following hyperparameters were used during training:
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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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  - lr_scheduler_warmup_steps: 200
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- - num_epochs: 0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - generated_from_trainer
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  datasets:
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  - glue
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+ metrics:
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+ - accuracy
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  model-index:
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  - name: t5-base_cola_dense
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: glue
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+ type: glue
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+ config: cola
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+ split: validation
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+ args: cola
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.8360498561840843
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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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  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the glue dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5247
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+ - Accuracy: 0.8360
 
 
 
 
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  ## Model description
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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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  - lr_scheduler_warmup_steps: 200
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.6253 | 0.07 | 10 | 0.6218 | 0.6913 |
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+ | 0.6283 | 0.15 | 20 | 0.6207 | 0.6913 |
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+ | 0.6297 | 0.22 | 30 | 0.6193 | 0.6913 |
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+ | 0.5936 | 0.3 | 40 | 0.6200 | 0.6913 |
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+ | 0.609 | 0.37 | 50 | 0.6216 | 0.6913 |
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+ | 0.5818 | 0.45 | 60 | 0.6182 | 0.6913 |
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+ | 0.5984 | 0.52 | 70 | 0.6139 | 0.6913 |
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+ | 0.5961 | 0.6 | 80 | 0.6089 | 0.6913 |
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+ | 0.5594 | 0.67 | 90 | 0.6168 | 0.6913 |
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+ | 0.597 | 0.75 | 100 | 0.6022 | 0.6913 |
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+ | 0.6092 | 0.82 | 110 | 0.5537 | 0.6903 |
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+ | 0.5454 | 0.9 | 120 | 0.5120 | 0.7296 |
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+ | 0.5368 | 0.97 | 130 | 0.5120 | 0.7584 |
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+ | 0.5185 | 1.04 | 140 | 0.4615 | 0.7987 |
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+ | 0.4664 | 1.12 | 150 | 0.4893 | 0.7977 |
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+ | 0.4938 | 1.19 | 160 | 0.4793 | 0.8044 |
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+ | 0.3994 | 1.27 | 170 | 0.4912 | 0.8025 |
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+ | 0.4989 | 1.34 | 180 | 0.5515 | 0.8092 |
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+ | 0.4709 | 1.42 | 190 | 0.4909 | 0.8054 |
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+ | 0.4099 | 1.49 | 200 | 0.5397 | 0.8121 |
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+ | 0.4671 | 1.57 | 210 | 0.4736 | 0.8102 |
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+ | 0.3893 | 1.64 | 220 | 0.4803 | 0.8178 |
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+ | 0.4027 | 1.72 | 230 | 0.5195 | 0.8159 |
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+ | 0.4208 | 1.79 | 240 | 0.4521 | 0.8188 |
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+ | 0.4506 | 1.87 | 250 | 0.4943 | 0.8188 |
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+ | 0.3647 | 1.94 | 260 | 0.4650 | 0.8255 |
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+ | 0.4223 | 2.01 | 270 | 0.4865 | 0.8284 |
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+ | 0.3584 | 2.09 | 280 | 0.4639 | 0.8284 |
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+ | 0.3555 | 2.16 | 290 | 0.5321 | 0.8236 |
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+ | 0.3433 | 2.24 | 300 | 0.5174 | 0.8303 |
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+ | 0.3904 | 2.31 | 310 | 0.4811 | 0.8274 |
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+ | 0.3418 | 2.39 | 320 | 0.5135 | 0.8265 |
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+ | 0.3397 | 2.46 | 330 | 0.4854 | 0.8322 |
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+ | 0.3336 | 2.54 | 340 | 0.5008 | 0.8332 |
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+ | 0.3471 | 2.61 | 350 | 0.5065 | 0.8293 |
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+ | 0.382 | 2.69 | 360 | 0.4708 | 0.8274 |
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+ | 0.3533 | 2.76 | 370 | 0.4862 | 0.8265 |
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+ | 0.3199 | 2.84 | 380 | 0.4904 | 0.8293 |
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+ | 0.3757 | 2.91 | 390 | 0.4970 | 0.8332 |
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+ | 0.3726 | 2.99 | 400 | 0.4965 | 0.8322 |
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+ | 0.2957 | 3.06 | 410 | 0.4628 | 0.8303 |
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+ | 0.3232 | 3.13 | 420 | 0.5174 | 0.8322 |
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+ | 0.2836 | 3.21 | 430 | 0.5038 | 0.8351 |
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+ | 0.2919 | 3.28 | 440 | 0.4987 | 0.8341 |
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+ | 0.3578 | 3.36 | 450 | 0.5187 | 0.8313 |
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+ | 0.398 | 3.43 | 460 | 0.5285 | 0.8380 |
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+ | 0.3024 | 3.51 | 470 | 0.4971 | 0.8351 |
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+ | 0.3153 | 3.58 | 480 | 0.5084 | 0.8351 |
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+ | 0.307 | 3.66 | 490 | 0.5371 | 0.8332 |
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+ | 0.2753 | 3.73 | 500 | 0.5247 | 0.8360 |
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+ | 0.3515 | 3.81 | 510 | 0.4782 | 0.8360 |
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+ | 0.2881 | 3.88 | 520 | 0.4784 | 0.8389 |
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+ | 0.3203 | 3.96 | 530 | 0.5115 | 0.8351 |
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+ | 0.2791 | 4.03 | 540 | 0.5294 | 0.8360 |
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+ | 0.301 | 4.1 | 550 | 0.5218 | 0.8322 |
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+ | 0.2652 | 4.18 | 560 | 0.4956 | 0.8360 |
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+ | 0.2954 | 4.25 | 570 | 0.4878 | 0.8332 |
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+ | 0.2345 | 4.33 | 580 | 0.5190 | 0.8313 |
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+ | 0.3762 | 4.4 | 590 | 0.5315 | 0.8351 |
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+ | 0.3614 | 4.48 | 600 | 0.5200 | 0.8341 |
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+ | 0.3178 | 4.55 | 610 | 0.5237 | 0.8341 |
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+ | 0.306 | 4.63 | 620 | 0.5232 | 0.8341 |
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+ | 0.2828 | 4.7 | 630 | 0.5278 | 0.8360 |
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+ | 0.3442 | 4.78 | 640 | 0.5270 | 0.8360 |
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+ | 0.3268 | 4.85 | 650 | 0.5252 | 0.8351 |
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+ | 0.2959 | 4.93 | 660 | 0.5284 | 0.8370 |
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+ | 0.2861 | 5.0 | 670 | 0.5277 | 0.8351 |
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+
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  ### Framework versions
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