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cost_to_hit_frequency_2128/README.md

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+---
+library_name: transformers
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: exceptions_exp2_cost_to_hit_frequency_2128
+  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/bzy2btrz)
+# exceptions_exp2_cost_to_hit_frequency_2128
+
+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: 3.5464
+- Accuracy: 0.3716
+
+## 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: 2128
+- 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 |
+|:-------------:|:-------:|:-----:|:---------------:|:--------:|
+| 4.836         | 0.2913  | 1000  | 4.7542          | 0.2547   |
+| 4.3423        | 0.5826  | 2000  | 4.2851          | 0.2993   |
+| 4.1418        | 0.8739  | 3000  | 4.0991          | 0.3155   |
+| 3.991         | 1.1652  | 4000  | 3.9937          | 0.3253   |
+| 3.9297        | 1.4565  | 5000  | 3.9147          | 0.3317   |
+| 3.8779        | 1.7478  | 6000  | 3.8544          | 0.3371   |
+| 3.7427        | 2.0390  | 7000  | 3.8124          | 0.3416   |
+| 3.7492        | 2.3303  | 8000  | 3.7812          | 0.3447   |
+| 3.7372        | 2.6216  | 9000  | 3.7514          | 0.3474   |
+| 3.7056        | 2.9130  | 10000 | 3.7261          | 0.3498   |
+| 3.6285        | 3.2042  | 11000 | 3.7096          | 0.3520   |
+| 3.638         | 3.4955  | 12000 | 3.6934          | 0.3540   |
+| 3.616         | 3.7868  | 13000 | 3.6741          | 0.3555   |
+| 3.5277        | 4.0781  | 14000 | 3.6672          | 0.3568   |
+| 3.5578        | 4.3694  | 15000 | 3.6568          | 0.3576   |
+| 3.5631        | 4.6607  | 16000 | 3.6425          | 0.3594   |
+| 3.5746        | 4.9520  | 17000 | 3.6273          | 0.3607   |
+| 3.4835        | 5.2432  | 18000 | 3.6301          | 0.3612   |
+| 3.5199        | 5.5345  | 19000 | 3.6201          | 0.3620   |
+| 3.5176        | 5.8259  | 20000 | 3.6096          | 0.3629   |
+| 3.4233        | 6.1171  | 21000 | 3.6123          | 0.3633   |
+| 3.4484        | 6.4084  | 22000 | 3.6026          | 0.3641   |
+| 3.4673        | 6.6997  | 23000 | 3.5936          | 0.3648   |
+| 3.4728        | 6.9910  | 24000 | 3.5832          | 0.3659   |
+| 3.4081        | 7.2823  | 25000 | 3.5913          | 0.3657   |
+| 3.4258        | 7.5736  | 26000 | 3.5828          | 0.3662   |
+| 3.4357        | 7.8649  | 27000 | 3.5754          | 0.3675   |
+| 3.3437        | 8.1561  | 28000 | 3.5826          | 0.3672   |
+| 3.3846        | 8.4474  | 29000 | 3.5741          | 0.3677   |
+| 3.397         | 8.7388  | 30000 | 3.5654          | 0.3687   |
+| 3.2933        | 9.0300  | 31000 | 3.5706          | 0.3684   |
+| 3.3448        | 9.3213  | 32000 | 3.5704          | 0.3688   |
+| 3.362         | 9.6126  | 33000 | 3.5591          | 0.3695   |
+| 3.3664        | 9.9039  | 34000 | 3.5527          | 0.3701   |
+| 3.3097        | 10.1952 | 35000 | 3.5632          | 0.3696   |
+| 3.3296        | 10.4865 | 36000 | 3.5596          | 0.3700   |
+| 3.3485        | 10.7778 | 37000 | 3.5511          | 0.3708   |
+| 3.2602        | 11.0690 | 38000 | 3.5577          | 0.3708   |
+| 3.2927        | 11.3603 | 39000 | 3.5537          | 0.3709   |
+| 3.2965        | 11.6517 | 40000 | 3.5464          | 0.3716   |
+| 3.3093        | 11.9430 | 41000 | 3.5401          | 0.3719   |
+| 3.2578        | 12.2342 | 42000 | 3.5509          | 0.3716   |
+| 3.2759        | 12.5255 | 43000 | 3.5461          | 0.3721   |
+| 3.2977        | 12.8168 | 44000 | 3.5381          | 0.3729   |
+| 3.2116        | 13.1081 | 45000 | 3.5465          | 0.3725   |
+| 3.2472        | 13.3994 | 46000 | 3.5436          | 0.3728   |
+| 3.2546        | 13.6907 | 47000 | 3.5366          | 0.3733   |
+| 3.2669        | 13.9820 | 48000 | 3.5320          | 0.3736   |
+| 3.2044        | 14.2732 | 49000 | 3.5419          | 0.3734   |
+| 3.2363        | 14.5646 | 50000 | 3.5365          | 0.3739   |
+| 3.2326        | 14.8559 | 51000 | 3.5300          | 0.3743   |
+| 3.1742        | 15.1471 | 52000 | 3.5394          | 0.3739   |
+| 3.2021        | 15.4384 | 53000 | 3.5332          | 0.3746   |
+| 3.2011        | 15.7297 | 54000 | 3.5290          | 0.3747   |
+| 3.1483        | 16.0210 | 55000 | 3.5327          | 0.3748   |
+| 3.16          | 16.3123 | 56000 | 3.5347          | 0.3746   |
+| 3.1867        | 16.6036 | 57000 | 3.5271          | 0.3752   |
+| 3.1814        | 16.8949 | 58000 | 3.5252          | 0.3755   |
+| 3.1394        | 17.1861 | 59000 | 3.5330          | 0.3753   |
+| 3.1498        | 17.4775 | 60000 | 3.5306          | 0.3755   |
+| 3.1687        | 17.7688 | 61000 | 3.5236          | 0.3760   |
+| 3.1237        | 18.0600 | 62000 | 3.5266          | 0.3760   |
+| 3.1318        | 18.3513 | 63000 | 3.5264          | 0.3759   |
+| 3.1343        | 18.6426 | 64000 | 3.5249          | 0.3764   |
+| 3.1352        | 18.9339 | 65000 | 3.5207          | 0.3766   |
+| 3.1114        | 19.2252 | 66000 | 3.5234          | 0.3765   |
+| 3.129         | 19.5165 | 67000 | 3.5234          | 0.3766   |
+| 3.1103        | 19.8078 | 68000 | 3.5216          | 0.3767   |
+
+
+### Framework versions
+
+- Transformers 4.55.2
+- Pytorch 2.8.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.21.4

cost_to_hit_frequency_2128/all_results.json

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+    "epoch": 20.0,
+    "eval_accuracy": 0.3716299568587447,
+    "eval_loss": 3.5464437007904053,
+    "eval_runtime": 179.9004,
+    "eval_samples": 16644,
+    "eval_samples_per_second": 92.518,
+    "eval_steps_per_second": 5.787,
+    "perplexity": 34.689730800009954,
+    "total_flos": 1.43513603407872e+18,
+    "train_loss": 3.438410573848788,
+    "train_runtime": 137369.0027,
+    "train_samples": 274623,
+    "train_samples_per_second": 39.983,
+    "train_steps_per_second": 0.5
+}

cost_to_hit_frequency_2128/checkpoint-30000/config.json

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

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