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cost_to_hit_frequency_1001/README.md

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+---
+library_name: transformers
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: exceptions_exp2_cost_to_hit_frequency_1001
+  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/3l1xx9yi)
+# exceptions_exp2_cost_to_hit_frequency_1001
+
+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: 3.5636
+- Accuracy: 0.3686
+
+## 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: 1001
+- 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 |
+|:-------------:|:-------:|:-----:|:---------------:|:--------:|
+| 4.8133        | 0.2913  | 1000  | 4.7542          | 0.2546   |
+| 4.3432        | 0.5826  | 2000  | 4.2874          | 0.2989   |
+| 4.1316        | 0.8739  | 3000  | 4.0944          | 0.3165   |
+| 3.9856        | 1.1652  | 4000  | 3.9840          | 0.3263   |
+| 3.9435        | 1.4565  | 5000  | 3.9095          | 0.3328   |
+| 3.8665        | 1.7478  | 6000  | 3.8514          | 0.3380   |
+| 3.7491        | 2.0390  | 7000  | 3.8110          | 0.3422   |
+| 3.7416        | 2.3303  | 8000  | 3.7788          | 0.3452   |
+| 3.728         | 2.6216  | 9000  | 3.7483          | 0.3481   |
+| 3.6958        | 2.9130  | 10000 | 3.7223          | 0.3504   |
+| 3.6306        | 3.2042  | 11000 | 3.7092          | 0.3524   |
+| 3.6245        | 3.4955  | 12000 | 3.6911          | 0.3540   |
+| 3.6365        | 3.7868  | 13000 | 3.6707          | 0.3559   |
+| 3.5138        | 4.0781  | 14000 | 3.6639          | 0.3571   |
+| 3.5632        | 4.3694  | 15000 | 3.6536          | 0.3582   |
+| 3.5684        | 4.6607  | 16000 | 3.6403          | 0.3597   |
+| 3.5646        | 4.9520  | 17000 | 3.6256          | 0.3607   |
+| 3.4892        | 5.2432  | 18000 | 3.6295          | 0.3613   |
+| 3.5037        | 5.5345  | 19000 | 3.6170          | 0.3623   |
+| 3.5029        | 5.8259  | 20000 | 3.6043          | 0.3632   |
+| 3.4399        | 6.1171  | 21000 | 3.6081          | 0.3638   |
+| 3.4438        | 6.4084  | 22000 | 3.6000          | 0.3643   |
+| 3.4622        | 6.6997  | 23000 | 3.5912          | 0.3649   |
+| 3.4761        | 6.9910  | 24000 | 3.5797          | 0.3658   |
+| 3.4035        | 7.2823  | 25000 | 3.5883          | 0.3660   |
+| 3.4207        | 7.5736  | 26000 | 3.5815          | 0.3664   |
+| 3.4351        | 7.8649  | 27000 | 3.5713          | 0.3672   |
+| 3.3456        | 8.1561  | 28000 | 3.5803          | 0.3676   |
+| 3.379         | 8.4474  | 29000 | 3.5733          | 0.3679   |
+| 3.3926        | 8.7388  | 30000 | 3.5636          | 0.3686   |
+| 3.305         | 9.0300  | 31000 | 3.5686          | 0.3689   |
+| 3.333         | 9.3213  | 32000 | 3.5682          | 0.3688   |
+| 3.3523        | 9.6126  | 33000 | 3.5594          | 0.3696   |
+| 3.3654        | 9.9039  | 34000 | 3.5503          | 0.3704   |
+| 3.2834        | 10.1952 | 35000 | 3.5617          | 0.3699   |
+| 3.3313        | 10.4865 | 36000 | 3.5552          | 0.3705   |
+| 3.3399        | 10.7778 | 37000 | 3.5458          | 0.3711   |
+| 3.2605        | 11.0690 | 38000 | 3.5583          | 0.3707   |
+| 3.2906        | 11.3603 | 39000 | 3.5520          | 0.3713   |
+| 3.3088        | 11.6517 | 40000 | 3.5464          | 0.3716   |
+| 3.3188        | 11.9430 | 41000 | 3.5363          | 0.3725   |
+| 3.2678        | 12.2342 | 42000 | 3.5468          | 0.3721   |
+| 3.2818        | 12.5255 | 43000 | 3.5414          | 0.3725   |
+| 3.2772        | 12.8168 | 44000 | 3.5360          | 0.3733   |
+| 3.215         | 13.1081 | 45000 | 3.5467          | 0.3725   |
+| 3.2448        | 13.3994 | 46000 | 3.5431          | 0.3730   |
+| 3.2716        | 13.6907 | 47000 | 3.5369          | 0.3734   |
+| 3.2672        | 13.9820 | 48000 | 3.5273          | 0.3741   |
+| 3.2114        | 14.2732 | 49000 | 3.5384          | 0.3735   |
+| 3.229         | 14.5646 | 50000 | 3.5358          | 0.3739   |
+| 3.2327        | 14.8559 | 51000 | 3.5292          | 0.3745   |
+| 3.1816        | 15.1471 | 52000 | 3.5386          | 0.3739   |
+| 3.1996        | 15.4384 | 53000 | 3.5346          | 0.3745   |
+| 3.2029        | 15.7297 | 54000 | 3.5301          | 0.3748   |
+| 3.151         | 16.0210 | 55000 | 3.5326          | 0.3749   |
+| 3.1679        | 16.3123 | 56000 | 3.5329          | 0.3749   |
+| 3.183         | 16.6036 | 57000 | 3.5285          | 0.3752   |
+| 3.1985        | 16.8949 | 58000 | 3.5229          | 0.3757   |
+| 3.1424        | 17.1861 | 59000 | 3.5316          | 0.3755   |
+| 3.1539        | 17.4775 | 60000 | 3.5271          | 0.3757   |
+| 3.1562        | 17.7688 | 61000 | 3.5240          | 0.3760   |
+| 3.1148        | 18.0600 | 62000 | 3.5264          | 0.3759   |
+| 3.1308        | 18.3513 | 63000 | 3.5277          | 0.3758   |
+| 3.1398        | 18.6426 | 64000 | 3.5230          | 0.3763   |
+| 3.1284        | 18.9339 | 65000 | 3.5189          | 0.3767   |
+| 3.1103        | 19.2252 | 66000 | 3.5239          | 0.3765   |
+| 3.1006        | 19.5165 | 67000 | 3.5217          | 0.3768   |
+| 3.1085        | 19.8078 | 68000 | 3.5209          | 0.3769   |
+
+
+### Framework versions
+
+- Transformers 4.55.2
+- Pytorch 2.8.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.21.4

cost_to_hit_frequency_1001/all_results.json

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+    "epoch": 20.0,
+    "eval_accuracy": 0.3686057656808563,
+    "eval_loss": 3.563647985458374,
+    "eval_runtime": 180.1205,
+    "eval_samples": 16644,
+    "eval_samples_per_second": 92.405,
+    "eval_steps_per_second": 5.779,
+    "perplexity": 35.29170623409146,
+    "total_flos": 1.43513603407872e+18,
+    "train_loss": 3.4363333413179364,
+    "train_runtime": 137192.0021,
+    "train_samples": 274623,
+    "train_samples_per_second": 40.035,
+    "train_steps_per_second": 0.5
+}

cost_to_hit_frequency_1001/checkpoint-30000/config.json

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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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