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cost_to_drop_frequency_3591/README.md

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+---
+library_name: transformers
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: exceptions_exp2_cost_to_drop_frequency_3591
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/craaaa/exceptions_exp2/runs/zfnnltyk)
+# exceptions_exp2_cost_to_drop_frequency_3591
+
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 3.5466
+- Accuracy: 0.3712
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0006
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 3591
+- gradient_accumulation_steps: 5
+- total_train_batch_size: 80
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 100
+- num_epochs: 20.0
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step  | Validation Loss | Accuracy |
+|:-------------:|:-------:|:-----:|:---------------:|:--------:|
+| 4.8266        | 0.2912  | 1000  | 4.7609          | 0.2540   |
+| 4.3399        | 0.5824  | 2000  | 4.2839          | 0.2997   |
+| 4.1452        | 0.8736  | 3000  | 4.0952          | 0.3154   |
+| 3.9904        | 1.1645  | 4000  | 3.9894          | 0.3254   |
+| 3.934         | 1.4557  | 5000  | 3.9117          | 0.3320   |
+| 3.8774        | 1.7469  | 6000  | 3.8566          | 0.3372   |
+| 3.7556        | 2.0379  | 7000  | 3.8100          | 0.3419   |
+| 3.7524        | 2.3290  | 8000  | 3.7799          | 0.3446   |
+| 3.7339        | 2.6202  | 9000  | 3.7507          | 0.3473   |
+| 3.7167        | 2.9114  | 10000 | 3.7239          | 0.3496   |
+| 3.6204        | 3.2024  | 11000 | 3.7100          | 0.3516   |
+| 3.6358        | 3.4936  | 12000 | 3.6902          | 0.3536   |
+| 3.6319        | 3.7848  | 13000 | 3.6755          | 0.3554   |
+| 3.5213        | 4.0757  | 14000 | 3.6668          | 0.3567   |
+| 3.5562        | 4.3669  | 15000 | 3.6574          | 0.3576   |
+| 3.5583        | 4.6581  | 16000 | 3.6411          | 0.3589   |
+| 3.5668        | 4.9493  | 17000 | 3.6268          | 0.3604   |
+| 3.4813        | 5.2402  | 18000 | 3.6294          | 0.3607   |
+| 3.4996        | 5.5314  | 19000 | 3.6198          | 0.3617   |
+| 3.5032        | 5.8226  | 20000 | 3.6066          | 0.3630   |
+| 3.4196        | 6.1136  | 21000 | 3.6101          | 0.3632   |
+| 3.4499        | 6.4048  | 22000 | 3.6030          | 0.3639   |
+| 3.4726        | 6.6959  | 23000 | 3.5935          | 0.3646   |
+| 3.4734        | 6.9871  | 24000 | 3.5827          | 0.3657   |
+| 3.4074        | 7.2781  | 25000 | 3.5906          | 0.3655   |
+| 3.4173        | 7.5693  | 26000 | 3.5848          | 0.3662   |
+| 3.4318        | 7.8605  | 27000 | 3.5727          | 0.3670   |
+| 3.3639        | 8.1514  | 28000 | 3.5807          | 0.3670   |
+| 3.3846        | 8.4426  | 29000 | 3.5724          | 0.3678   |
+| 3.3893        | 8.7338  | 30000 | 3.5643          | 0.3683   |
+| 3.2997        | 9.0248  | 31000 | 3.5693          | 0.3684   |
+| 3.3417        | 9.3159  | 32000 | 3.5689          | 0.3688   |
+| 3.3562        | 9.6071  | 33000 | 3.5602          | 0.3692   |
+| 3.3759        | 9.8983  | 34000 | 3.5531          | 0.3698   |
+| 3.3017        | 10.1893 | 35000 | 3.5646          | 0.3693   |
+| 3.3164        | 10.4805 | 36000 | 3.5563          | 0.3701   |
+| 3.3349        | 10.7716 | 37000 | 3.5481          | 0.3709   |
+| 3.2621        | 11.0626 | 38000 | 3.5563          | 0.3708   |
+| 3.2832        | 11.3538 | 39000 | 3.5536          | 0.3709   |
+| 3.3111        | 11.6450 | 40000 | 3.5466          | 0.3712   |
+| 3.326         | 11.9362 | 41000 | 3.5387          | 0.3720   |
+| 3.2475        | 12.2271 | 42000 | 3.5505          | 0.3716   |
+| 3.2651        | 12.5183 | 43000 | 3.5456          | 0.3720   |
+| 3.2914        | 12.8095 | 44000 | 3.5383          | 0.3727   |
+| 3.2055        | 13.1005 | 45000 | 3.5462          | 0.3721   |
+| 3.2443        | 13.3916 | 46000 | 3.5427          | 0.3727   |
+| 3.2584        | 13.6828 | 47000 | 3.5379          | 0.3731   |
+| 3.2518        | 13.9740 | 48000 | 3.5292          | 0.3737   |
+| 3.2007        | 14.2650 | 49000 | 3.5400          | 0.3732   |
+| 3.2079        | 14.5562 | 50000 | 3.5364          | 0.3738   |
+| 3.245         | 14.8474 | 51000 | 3.5287          | 0.3741   |
+| 3.1718        | 15.1383 | 52000 | 3.5387          | 0.3737   |
+| 3.1904        | 15.4295 | 53000 | 3.5351          | 0.3743   |
+| 3.2151        | 15.7207 | 54000 | 3.5294          | 0.3743   |
+| 3.1627        | 16.0116 | 55000 | 3.5313          | 0.3746   |
+| 3.1717        | 16.3028 | 56000 | 3.5333          | 0.3746   |
+| 3.1887        | 16.5940 | 57000 | 3.5278          | 0.3750   |
+| 3.1901        | 16.8852 | 58000 | 3.5243          | 0.3755   |
+| 3.1498        | 17.1762 | 59000 | 3.5312          | 0.3752   |
+| 3.153         | 17.4674 | 60000 | 3.5281          | 0.3755   |
+| 3.1645        | 17.7585 | 61000 | 3.5239          | 0.3757   |
+| 3.1118        | 18.0495 | 62000 | 3.5264          | 0.3757   |
+| 3.1376        | 18.3407 | 63000 | 3.5263          | 0.3759   |
+| 3.1384        | 18.6319 | 64000 | 3.5238          | 0.3761   |
+| 3.1365        | 18.9231 | 65000 | 3.5200          | 0.3765   |
+| 3.1048        | 19.2140 | 66000 | 3.5228          | 0.3764   |
+| 3.1252        | 19.5052 | 67000 | 3.5221          | 0.3765   |
+| 3.1198        | 19.7964 | 68000 | 3.5209          | 0.3766   |
+
+
+### Framework versions
+
+- Transformers 4.55.2
+- Pytorch 2.8.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.21.4

cost_to_drop_frequency_3591/all_results.json

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+{
+    "epoch": 20.0,
+    "eval_accuracy": 0.371191240289195,
+    "eval_loss": 3.5466434955596924,
+    "eval_runtime": 179.6731,
+    "eval_samples": 16643,
+    "eval_samples_per_second": 92.629,
+    "eval_steps_per_second": 5.794,
+    "perplexity": 34.69666231918936,
+    "total_flos": 1.43566384398336e+18,
+    "train_loss": 3.438475851533715,
+    "train_runtime": 136533.177,
+    "train_samples": 274724,
+    "train_samples_per_second": 40.243,
+    "train_steps_per_second": 0.503
+}

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+  "summary_proj_to_labels": true,
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+  "summary_use_proj": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.2",
+  "use_cache": true,
+  "vocab_size": 50257
+}

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