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flan-t5laa2-small

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  1. README.md +170 -195
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  3. model.safetensors +1 -1
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README.md CHANGED
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  ---
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  library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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- ## Model Details
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-
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- ### Downstream Use [optional]
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- ## Bias, Risks, and Limitations
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- ## Training Details
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- ### Training Data
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- ## Evaluation
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- #### Factors
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- #### Metrics
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- ## Environmental Impact
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Cloud Provider:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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1
  ---
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  library_name: transformers
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+ base_model: hrezaei/flan-t5laa2-small
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - generator
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: flan-t5laa2-small
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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
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # flan-t5laa2-small
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+
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+ This model is a fine-tuned version of [hrezaei/flan-t5laa2-small](https://huggingface.co/hrezaei/flan-t5laa2-small) on the generator dataset.
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+ It achieves the following results on the evaluation set:
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+ - Perplexity: 1.2301
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+ - Loss: 0.2071
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+ - Accuracy: 0.0032
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+ - Lookahead Perplexity: 521.5793
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+ - Lookahead Loss: 6.2569
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+ - Base Perplexity: 1.2138
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+ - Base Loss: 0.1937
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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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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - distributed_type: multi-GPU
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+ - num_devices: 2
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+ - total_train_batch_size: 32
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+ - total_eval_batch_size: 32
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+ - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - training_steps: 524288
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Perplexity | Validation Loss | Accuracy | Lookahead Perplexity | Lookahead Loss | Base Perplexity | Base Loss |
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+ |:-------------:|:------:|:------:|:----------:|:---------------:|:--------:|:--------------------:|:--------------:|:---------------:|:---------:|
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+ | 0.6126 | 0.0095 | 5000 | 1.2367 | 0.2124 | 0.0032 | 8068.5739 | 8.9957 | 1.2138 | 0.1937 |
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+ | 0.6026 | 0.0191 | 10000 | 1.2345 | 0.2107 | 0.0032 | 3266.5187 | 8.0915 | 1.2138 | 0.1937 |
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+ | 0.6282 | 0.0286 | 15000 | 1.2334 | 0.2098 | 0.0032 | 2089.9703 | 7.6449 | 1.2138 | 0.1937 |
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+ | 0.6053 | 0.0381 | 20000 | 1.2329 | 0.2094 | 0.0032 | 1657.4587 | 7.4130 | 1.2138 | 0.1937 |
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+ | 0.6244 | 0.0477 | 25000 | 1.2326 | 0.2091 | 0.0032 | 1433.9858 | 7.2682 | 1.2138 | 0.1937 |
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+ | 0.6301 | 0.0572 | 30000 | 1.2323 | 0.2089 | 0.0032 | 1296.7085 | 7.1676 | 1.2138 | 0.1937 |
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+ | 0.6135 | 0.0668 | 35000 | 1.2321 | 0.2087 | 0.0032 | 1204.5739 | 7.0939 | 1.2138 | 0.1937 |
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+ | 0.5998 | 0.0763 | 40000 | 1.2320 | 0.2086 | 0.0032 | 1134.1594 | 7.0336 | 1.2138 | 0.1937 |
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+ | 0.5972 | 0.0858 | 45000 | 1.2319 | 0.2085 | 0.0032 | 1079.1354 | 6.9839 | 1.2138 | 0.1937 |
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+ | 0.5947 | 0.0954 | 50000 | 1.2318 | 0.2085 | 0.0032 | 1038.1346 | 6.9452 | 1.2138 | 0.1937 |
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+ | 0.6257 | 0.1049 | 55000 | 1.2317 | 0.2084 | 0.0032 | 1001.9005 | 6.9097 | 1.2138 | 0.1937 |
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+ | 0.6075 | 0.1144 | 60000 | 1.2316 | 0.2083 | 0.0032 | 971.3019 | 6.8786 | 1.2138 | 0.1937 |
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+ | 0.6094 | 1.0048 | 65000 | 1.2315 | 0.2083 | 0.0032 | 944.4363 | 6.8506 | 1.2138 | 0.1937 |
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+ | 0.6082 | 1.0143 | 70000 | 1.2315 | 0.2082 | 0.0032 | 920.2534 | 6.8246 | 1.2138 | 0.1937 |
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+ | 0.6047 | 1.0238 | 75000 | 1.2314 | 0.2082 | 0.0032 | 896.8586 | 6.7989 | 1.2138 | 0.1937 |
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+ | 0.6081 | 1.0334 | 80000 | 1.2314 | 0.2081 | 0.0032 | 877.9252 | 6.7776 | 1.2138 | 0.1937 |
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+ | 0.6175 | 1.0429 | 85000 | 1.2313 | 0.2081 | 0.0032 | 860.2578 | 6.7572 | 1.2138 | 0.1937 |
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+ | 0.606 | 1.0525 | 90000 | 1.2313 | 0.2080 | 0.0032 | 844.6464 | 6.7389 | 1.2138 | 0.1937 |
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+ | 0.6149 | 1.0620 | 95000 | 1.2312 | 0.2080 | 0.0032 | 828.3173 | 6.7194 | 1.2138 | 0.1937 |
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+ | 0.6122 | 1.0715 | 100000 | 1.2312 | 0.2080 | 0.0032 | 813.9770 | 6.7019 | 1.2138 | 0.1937 |
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+ | 0.6199 | 1.0811 | 105000 | 1.2311 | 0.2079 | 0.0032 | 799.4303 | 6.6839 | 1.2138 | 0.1937 |
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+ | 0.5994 | 1.0906 | 110000 | 1.2311 | 0.2079 | 0.0032 | 786.4358 | 6.6675 | 1.2138 | 0.1937 |
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+ | 0.6021 | 1.1001 | 115000 | 1.2311 | 0.2079 | 0.0032 | 775.5390 | 6.6536 | 1.2138 | 0.1937 |
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+ | 0.6186 | 1.1097 | 120000 | 1.2310 | 0.2078 | 0.0032 | 764.0533 | 6.6386 | 1.2138 | 0.1937 |
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+ | 0.596 | 1.1192 | 125000 | 1.2310 | 0.2078 | 0.0032 | 753.5015 | 6.6247 | 1.2138 | 0.1937 |
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+ | 0.6063 | 2.0095 | 130000 | 1.2310 | 0.2078 | 0.0032 | 743.7327 | 6.6117 | 1.2138 | 0.1937 |
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+ | 0.5996 | 2.0191 | 135000 | 1.2309 | 0.2078 | 0.0032 | 733.5159 | 6.5978 | 1.2138 | 0.1937 |
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+ | 0.6263 | 2.0286 | 140000 | 1.2309 | 0.2077 | 0.0032 | 724.4130 | 6.5854 | 1.2138 | 0.1937 |
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+ | 0.6015 | 2.0381 | 145000 | 1.2309 | 0.2077 | 0.0032 | 715.8316 | 6.5734 | 1.2138 | 0.1937 |
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+ | 0.6236 | 2.0477 | 150000 | 1.2308 | 0.2077 | 0.0032 | 708.4340 | 6.5631 | 1.2138 | 0.1937 |
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+ | 0.6288 | 2.0572 | 155000 | 1.2308 | 0.2077 | 0.0032 | 700.8450 | 6.5523 | 1.2138 | 0.1937 |
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+ | 0.6123 | 2.0668 | 160000 | 1.2308 | 0.2077 | 0.0032 | 693.3540 | 6.5415 | 1.2138 | 0.1937 |
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+ | 0.5964 | 2.0763 | 165000 | 1.2308 | 0.2076 | 0.0032 | 685.6423 | 6.5304 | 1.2138 | 0.1937 |
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+ | 0.5974 | 2.0858 | 170000 | 1.2307 | 0.2076 | 0.0032 | 678.1766 | 6.5194 | 1.2138 | 0.1937 |
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+ | 0.5933 | 2.0954 | 175000 | 1.2307 | 0.2076 | 0.0032 | 672.4544 | 6.5109 | 1.2138 | 0.1937 |
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+ | 0.6239 | 2.1049 | 180000 | 1.2307 | 0.2076 | 0.0032 | 666.2288 | 6.5016 | 1.2138 | 0.1937 |
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+ | 0.6071 | 2.1144 | 185000 | 1.2307 | 0.2076 | 0.0032 | 660.4713 | 6.4930 | 1.2138 | 0.1937 |
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+ | 0.6087 | 3.0048 | 190000 | 1.2306 | 0.2075 | 0.0032 | 654.7854 | 6.4843 | 1.2138 | 0.1937 |
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+ | 0.6074 | 3.0143 | 195000 | 1.2306 | 0.2075 | 0.0032 | 649.3958 | 6.4760 | 1.2138 | 0.1937 |
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+ | 0.6046 | 3.0238 | 200000 | 1.2306 | 0.2075 | 0.0032 | 643.2597 | 6.4665 | 1.2138 | 0.1937 |
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+ | 0.6084 | 3.0334 | 205000 | 1.2306 | 0.2075 | 0.0032 | 638.5549 | 6.4592 | 1.2138 | 0.1937 |
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+ | 0.614 | 3.0429 | 210000 | 1.2306 | 0.2075 | 0.0032 | 633.8094 | 6.4517 | 1.2138 | 0.1937 |
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+ | 0.605 | 3.0525 | 215000 | 1.2305 | 0.2075 | 0.0032 | 629.8567 | 6.4455 | 1.2138 | 0.1937 |
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+ | 0.6158 | 3.0620 | 220000 | 1.2305 | 0.2074 | 0.0032 | 624.9755 | 6.4377 | 1.2138 | 0.1937 |
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+ | 0.6103 | 3.0715 | 225000 | 1.2305 | 0.2074 | 0.0032 | 620.7078 | 6.4309 | 1.2138 | 0.1937 |
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+ | 0.6174 | 3.0811 | 230000 | 1.2305 | 0.2074 | 0.0032 | 615.9860 | 6.4232 | 1.2138 | 0.1937 |
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+ | 0.5991 | 3.0906 | 235000 | 1.2305 | 0.2074 | 0.0032 | 611.7069 | 6.4163 | 1.2138 | 0.1937 |
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+ | 0.6033 | 3.1001 | 240000 | 1.2305 | 0.2074 | 0.0032 | 608.3681 | 6.4108 | 1.2138 | 0.1937 |
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+ | 0.6166 | 3.1097 | 245000 | 1.2305 | 0.2074 | 0.0032 | 604.4235 | 6.4043 | 1.2138 | 0.1937 |
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+ | 0.5941 | 3.1192 | 250000 | 1.2304 | 0.2074 | 0.0032 | 600.8208 | 6.3983 | 1.2138 | 0.1937 |
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+ | 0.6054 | 4.0095 | 255000 | 1.2304 | 0.2074 | 0.0032 | 597.4248 | 6.3926 | 1.2138 | 0.1937 |
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+ | 0.5991 | 4.0191 | 260000 | 1.2304 | 0.2073 | 0.0032 | 593.5735 | 6.3862 | 1.2138 | 0.1937 |
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+ | 0.6243 | 4.0286 | 265000 | 1.2304 | 0.2073 | 0.0032 | 590.2097 | 6.3805 | 1.2138 | 0.1937 |
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+ | 0.6023 | 4.0381 | 270000 | 1.2304 | 0.2073 | 0.0032 | 586.9999 | 6.3750 | 1.2138 | 0.1937 |
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+ | 0.6215 | 4.0477 | 275000 | 1.2304 | 0.2073 | 0.0032 | 584.3271 | 6.3705 | 1.2138 | 0.1937 |
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+ | 0.6266 | 4.0572 | 280000 | 1.2304 | 0.2073 | 0.0032 | 581.6015 | 6.3658 | 1.2138 | 0.1937 |
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+ | 0.6115 | 4.0668 | 285000 | 1.2303 | 0.2073 | 0.0032 | 578.7785 | 6.3609 | 1.2138 | 0.1937 |
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+ | 0.5971 | 4.0763 | 290000 | 1.2303 | 0.2073 | 0.0032 | 575.7152 | 6.3556 | 1.2138 | 0.1937 |
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+ | 0.5976 | 4.0858 | 295000 | 1.2303 | 0.2073 | 0.0032 | 572.6544 | 6.3503 | 1.2138 | 0.1937 |
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+ | 0.594 | 4.0954 | 300000 | 1.2303 | 0.2073 | 0.0032 | 570.5147 | 6.3465 | 1.2138 | 0.1937 |
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+ | 0.6216 | 4.1049 | 305000 | 1.2303 | 0.2073 | 0.0032 | 568.0579 | 6.3422 | 1.2138 | 0.1937 |
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+ | 0.6069 | 4.1144 | 310000 | 1.2303 | 0.2073 | 0.0032 | 565.8226 | 6.3383 | 1.2138 | 0.1937 |
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+ | 0.6083 | 5.0048 | 315000 | 1.2303 | 0.2072 | 0.0032 | 563.5451 | 6.3342 | 1.2138 | 0.1937 |
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+ | 0.6077 | 5.0143 | 320000 | 1.2303 | 0.2072 | 0.0032 | 561.3008 | 6.3303 | 1.2138 | 0.1937 |
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+ | 0.6048 | 5.0238 | 325000 | 1.2303 | 0.2072 | 0.0032 | 558.7242 | 6.3257 | 1.2138 | 0.1937 |
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+ | 0.6075 | 5.0334 | 330000 | 1.2303 | 0.2072 | 0.0032 | 556.8461 | 6.3223 | 1.2138 | 0.1937 |
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+ | 0.6143 | 5.0429 | 335000 | 1.2302 | 0.2072 | 0.0032 | 554.9196 | 6.3188 | 1.2138 | 0.1937 |
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+ | 0.6055 | 5.0525 | 340000 | 1.2302 | 0.2072 | 0.0032 | 553.4305 | 6.3161 | 1.2138 | 0.1937 |
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+ | 0.6154 | 5.0620 | 345000 | 1.2302 | 0.2072 | 0.0032 | 551.4133 | 6.3125 | 1.2138 | 0.1937 |
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+ | 0.6104 | 5.0715 | 350000 | 1.2302 | 0.2072 | 0.0032 | 549.7351 | 6.3094 | 1.2138 | 0.1937 |
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+ | 0.6214 | 5.0811 | 355000 | 1.2302 | 0.2072 | 0.0032 | 547.6832 | 6.3057 | 1.2138 | 0.1937 |
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+ | 0.6011 | 5.0906 | 360000 | 1.2302 | 0.2072 | 0.0032 | 545.9130 | 6.3025 | 1.2138 | 0.1937 |
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+ | 0.6025 | 5.1001 | 365000 | 1.2302 | 0.2072 | 0.0032 | 544.6638 | 6.3002 | 1.2138 | 0.1937 |
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+ | 0.616 | 5.1097 | 370000 | 1.2302 | 0.2072 | 0.0032 | 543.0777 | 6.2973 | 1.2138 | 0.1937 |
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+ | 0.5951 | 5.1192 | 375000 | 1.2302 | 0.2072 | 0.0032 | 541.6890 | 6.2947 | 1.2138 | 0.1937 |
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+ | 0.6068 | 6.0095 | 380000 | 1.2302 | 0.2072 | 0.0032 | 540.3254 | 6.2922 | 1.2138 | 0.1937 |
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+ | 0.5974 | 6.0191 | 385000 | 1.2302 | 0.2072 | 0.0032 | 538.8241 | 6.2894 | 1.2138 | 0.1937 |
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+ | 0.6267 | 6.0286 | 390000 | 1.2302 | 0.2072 | 0.0032 | 537.5214 | 6.2870 | 1.2138 | 0.1937 |
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+ | 0.5994 | 6.0381 | 395000 | 1.2302 | 0.2071 | 0.0032 | 536.2929 | 6.2847 | 1.2138 | 0.1937 |
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+ | 0.6225 | 6.0477 | 400000 | 1.2302 | 0.2071 | 0.0032 | 535.2460 | 6.2827 | 1.2138 | 0.1937 |
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+ | 0.629 | 6.0572 | 405000 | 1.2302 | 0.2071 | 0.0032 | 534.2133 | 6.2808 | 1.2138 | 0.1937 |
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+ | 0.6107 | 6.0668 | 410000 | 1.2301 | 0.2071 | 0.0032 | 533.1691 | 6.2788 | 1.2138 | 0.1937 |
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+ | 0.5961 | 6.0763 | 415000 | 1.2301 | 0.2071 | 0.0032 | 532.0580 | 6.2768 | 1.2138 | 0.1937 |
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+ | 0.5962 | 6.0858 | 420000 | 1.2301 | 0.2071 | 0.0032 | 530.9045 | 6.2746 | 1.2138 | 0.1937 |
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+ | 0.5928 | 6.0954 | 425000 | 1.2301 | 0.2071 | 0.0032 | 530.1710 | 6.2732 | 1.2138 | 0.1937 |
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+ | 0.6228 | 6.1049 | 430000 | 1.2301 | 0.2071 | 0.0032 | 529.3049 | 6.2716 | 1.2138 | 0.1937 |
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+ | 0.6064 | 6.1144 | 435000 | 1.2301 | 0.2071 | 0.0032 | 528.5336 | 6.2701 | 1.2138 | 0.1937 |
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+ | 0.608 | 7.0048 | 440000 | 1.2301 | 0.2071 | 0.0032 | 527.7939 | 6.2687 | 1.2138 | 0.1937 |
151
+ | 0.6074 | 7.0143 | 445000 | 1.2301 | 0.2071 | 0.0032 | 527.0764 | 6.2673 | 1.2138 | 0.1937 |
152
+ | 0.6062 | 7.0238 | 450000 | 1.2301 | 0.2071 | 0.0032 | 526.2217 | 6.2657 | 1.2138 | 0.1937 |
153
+ | 0.6076 | 7.0334 | 455000 | 1.2301 | 0.2071 | 0.0032 | 525.6428 | 6.2646 | 1.2138 | 0.1937 |
154
+ | 0.6147 | 7.0429 | 460000 | 1.2301 | 0.2071 | 0.0032 | 525.0607 | 6.2635 | 1.2138 | 0.1937 |
155
+ | 0.6053 | 7.0525 | 465000 | 1.2301 | 0.2071 | 0.0032 | 524.6511 | 6.2627 | 1.2138 | 0.1937 |
156
+ | 0.6144 | 7.0620 | 470000 | 1.2301 | 0.2071 | 0.0032 | 524.1066 | 6.2617 | 1.2138 | 0.1937 |
157
+ | 0.6112 | 7.0715 | 475000 | 1.2301 | 0.2071 | 0.0032 | 523.6863 | 6.2609 | 1.2138 | 0.1937 |
158
+ | 0.6218 | 7.0811 | 480000 | 1.2301 | 0.2071 | 0.0032 | 523.2272 | 6.2600 | 1.2138 | 0.1937 |
159
+ | 0.5981 | 7.0906 | 485000 | 1.2301 | 0.2071 | 0.0032 | 522.8427 | 6.2593 | 1.2138 | 0.1937 |
160
+ | 0.6033 | 7.1001 | 490000 | 1.2301 | 0.2071 | 0.0032 | 522.6008 | 6.2588 | 1.2138 | 0.1937 |
161
+ | 0.6172 | 7.1097 | 495000 | 1.2301 | 0.2071 | 0.0032 | 522.3052 | 6.2583 | 1.2138 | 0.1937 |
162
+ | 0.5926 | 7.1192 | 500000 | 1.2301 | 0.2071 | 0.0032 | 522.0964 | 6.2579 | 1.2138 | 0.1937 |
163
+ | 0.608 | 8.0095 | 505000 | 1.2301 | 0.2071 | 0.0032 | 521.9141 | 6.2575 | 1.2138 | 0.1937 |
164
+ | 0.5975 | 8.0191 | 510000 | 1.2301 | 0.2071 | 0.0032 | 521.7486 | 6.2572 | 1.2138 | 0.1937 |
165
+ | 0.6248 | 8.0286 | 515000 | 1.2301 | 0.2071 | 0.0032 | 521.6438 | 6.2570 | 1.2138 | 0.1937 |
166
+ | 0.6013 | 8.0381 | 520000 | 1.2301 | 0.2071 | 0.0032 | 521.5793 | 6.2569 | 1.2138 | 0.1937 |
167
+
168
+
169
+ ### Framework versions
170
+
171
+ - Transformers 4.57.0.dev0
172
+ - Pytorch 2.8.0+cu128
173
+ - Datasets 4.2.0
174
+ - Tokenizers 0.22.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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