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flan-t5laa2-large

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  1. README.md +170 -195
  2. emissions.csv +2 -0
  3. model.safetensors +1 -1
  4. runs/Nov20_13-52-10_gpu26.viking2.yor.alces.network/events.out.tfevents.1763647454.gpu26.viking2.yor.alces.network.3427557.0 +2 -2
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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- <!-- 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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- - **Developed by:** [More Information Needed]
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-
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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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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- ### Downstream Use [optional]
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- ### Out-of-Scope Use
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- ## Bias, Risks, and Limitations
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- ### Recommendations
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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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- [More Information Needed]
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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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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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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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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [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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- ## Citation [optional]
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- ## Glossary [optional]
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- ## More Information [optional]
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1
  ---
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  library_name: transformers
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+ base_model: hrezaei/flan-t5laa2-large
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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-large
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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-large
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+
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+ This model is a fine-tuned version of [hrezaei/flan-t5laa2-large](https://huggingface.co/hrezaei/flan-t5laa2-large) on the generator dataset.
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+ It achieves the following results on the evaluation set:
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+ - Perplexity: 1.1522
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+ - Loss: 0.1417
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+ - Accuracy: 0.0025
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+ - Lookahead Perplexity: 523.9337
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+ - Lookahead Loss: 6.2614
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+ - Base Perplexity: 1.1386
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+ - Base Loss: 0.1298
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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 | Accuracy | Base Loss | Base Perplexity | Lookahead Loss | Lookahead Perplexity | Validation Loss | Perplexity |
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+ |:-------------:|:------:|:------:|:--------:|:---------:|:---------------:|:--------------:|:--------------------:|:---------------:|:----------:|
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+ | 0.3249 | 0.0095 | 5000 | 0.0025 | 0.1298 | 1.1386 | 9.1628 | 9536.0974 | 0.1473 | 1.1587 |
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+ | 0.3149 | 0.0191 | 10000 | 0.0025 | 0.1298 | 1.1386 | 8.2987 | 4018.5587 | 0.1457 | 1.1568 |
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+ | 0.3455 | 0.0286 | 15000 | 0.0025 | 0.1298 | 1.1386 | 7.8543 | 2576.7294 | 0.1448 | 1.1558 |
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+ | 0.3164 | 0.0381 | 20000 | 0.0025 | 0.1298 | 1.1386 | 7.6043 | 2006.8105 | 0.1443 | 1.1552 |
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+ | 0.3412 | 0.0477 | 25000 | 0.0025 | 0.1298 | 1.1386 | 7.4405 | 1703.6775 | 0.1440 | 1.1549 |
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+ | 0.3334 | 0.0572 | 30000 | 0.0025 | 0.1298 | 1.1386 | 7.3224 | 1513.8265 | 0.1438 | 1.1546 |
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+ | 0.3182 | 0.0668 | 35000 | 0.0025 | 0.1298 | 1.1386 | 7.2325 | 1383.6223 | 0.1436 | 1.1544 |
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+ | 0.3193 | 0.0763 | 40000 | 0.0025 | 0.1298 | 1.1386 | 7.1599 | 1286.7995 | 0.1434 | 1.1542 |
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+ | 0.3112 | 0.0858 | 45000 | 0.0025 | 0.1298 | 1.1386 | 7.1003 | 1212.3711 | 0.1433 | 1.1541 |
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+ | 0.3084 | 0.0954 | 50000 | 0.0025 | 0.1298 | 1.1386 | 7.0527 | 1155.9677 | 0.1432 | 1.1540 |
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+ | 0.3281 | 0.1049 | 55000 | 0.0025 | 0.1298 | 1.1386 | 7.0097 | 1107.3615 | 0.1431 | 1.1539 |
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+ | 0.3096 | 0.1144 | 60000 | 0.0025 | 0.1298 | 1.1386 | 6.9716 | 1065.9302 | 0.1431 | 1.1538 |
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+ | 0.3168 | 1.0048 | 65000 | 0.0025 | 0.1298 | 1.1386 | 6.9373 | 1029.9387 | 0.1430 | 1.1537 |
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+ | 0.3158 | 1.0143 | 70000 | 0.0025 | 0.1298 | 1.1386 | 6.9058 | 998.0076 | 0.1429 | 1.1536 |
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+ | 0.3149 | 1.0238 | 75000 | 0.0025 | 0.1298 | 1.1386 | 6.8755 | 968.2614 | 0.1429 | 1.1536 |
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+ | 0.3185 | 1.0334 | 80000 | 0.0025 | 0.1298 | 1.1386 | 6.8495 | 943.4458 | 0.1428 | 1.1535 |
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+ | 0.3247 | 1.0429 | 85000 | 0.0025 | 0.1298 | 1.1386 | 6.8251 | 920.7070 | 0.1428 | 1.1535 |
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+ | 0.3166 | 1.0525 | 90000 | 0.0025 | 0.1298 | 1.1386 | 6.8033 | 900.7799 | 0.1427 | 1.1534 |
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+ | 0.3171 | 1.0620 | 95000 | 0.0025 | 0.1298 | 1.1386 | 6.7802 | 880.2640 | 0.1427 | 1.1534 |
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+ | 0.3247 | 1.0715 | 100000 | 0.0025 | 0.1298 | 1.1386 | 6.7589 | 861.6873 | 0.1426 | 1.1533 |
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+ | 0.3199 | 1.0095 | 105000 | 0.0025 | 0.1298 | 1.1386 | 6.7393 | 844.9302 | 0.1426 | 1.1533 |
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+ | 0.3116 | 1.0191 | 110000 | 0.0025 | 0.1298 | 1.1386 | 6.7202 | 828.9964 | 0.1426 | 1.1532 |
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+ | 0.3431 | 1.0286 | 115000 | 0.0025 | 0.1298 | 1.1386 | 6.7028 | 814.7008 | 0.1425 | 1.1532 |
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+ | 0.3145 | 1.0381 | 120000 | 0.0025 | 0.1298 | 1.1386 | 6.6864 | 801.3963 | 0.1425 | 1.1531 |
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+ | 0.3396 | 1.0477 | 125000 | 0.0025 | 0.1298 | 1.1386 | 6.6714 | 789.4998 | 0.1425 | 1.1531 |
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+ | 0.332 | 1.0572 | 130000 | 0.0025 | 0.1298 | 1.1386 | 6.6562 | 777.5850 | 0.1424 | 1.1531 |
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+ | 0.3169 | 1.0668 | 135000 | 0.0025 | 0.1298 | 1.1386 | 6.6411 | 765.9321 | 0.1424 | 1.1530 |
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+ | 0.3183 | 1.0763 | 140000 | 0.0025 | 0.1298 | 1.1386 | 6.6259 | 754.4168 | 0.1424 | 1.1530 |
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+ | 0.3102 | 1.0858 | 145000 | 0.0025 | 0.1298 | 1.1386 | 6.6117 | 743.7487 | 0.1423 | 1.1530 |
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+ | 0.3075 | 1.0954 | 150000 | 0.0025 | 0.1298 | 1.1386 | 6.6002 | 735.2481 | 0.1423 | 1.1530 |
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+ | 0.3272 | 1.1049 | 155000 | 0.0025 | 0.1298 | 1.1386 | 6.5881 | 726.3988 | 0.1423 | 1.1529 |
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+ | 0.3088 | 1.1144 | 160000 | 0.0025 | 0.1298 | 1.1386 | 6.5765 | 717.9999 | 0.1423 | 1.1529 |
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+ | 0.316 | 2.0048 | 165000 | 0.0025 | 0.1298 | 1.1386 | 6.5648 | 709.6853 | 0.1423 | 1.1529 |
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+ | 0.315 | 2.0143 | 170000 | 0.0025 | 0.1298 | 1.1386 | 6.5536 | 701.7924 | 0.1422 | 1.1528 |
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+ | 0.3142 | 2.0238 | 175000 | 0.0025 | 0.1298 | 1.1386 | 6.5417 | 693.4763 | 0.1422 | 1.1528 |
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+ | 0.3178 | 2.0334 | 180000 | 0.0025 | 0.1298 | 1.1386 | 6.5319 | 686.6713 | 0.1422 | 1.1528 |
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+ | 0.324 | 2.0429 | 185000 | 0.0025 | 0.1298 | 1.1386 | 6.5221 | 680.0125 | 0.1422 | 1.1528 |
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+ | 0.316 | 2.0525 | 190000 | 0.0025 | 0.1298 | 1.1386 | 6.5135 | 674.1869 | 0.1422 | 1.1528 |
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+ | 0.3165 | 2.0620 | 195000 | 0.0025 | 0.1298 | 1.1386 | 6.5032 | 667.2772 | 0.1421 | 1.1527 |
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+ | 0.3241 | 2.0715 | 200000 | 0.0025 | 0.1298 | 1.1386 | 6.4936 | 660.9243 | 0.1421 | 1.1527 |
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+ | 0.3166 | 1.0095 | 205000 | 0.0025 | 0.1298 | 1.1386 | 6.4848 | 655.1015 | 0.1421 | 1.1527 |
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+ | 0.3104 | 1.0191 | 210000 | 0.0025 | 0.1298 | 1.1386 | 6.4757 | 649.1789 | 0.1421 | 1.1527 |
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+ | 0.3416 | 1.0286 | 215000 | 0.0025 | 0.1298 | 1.1386 | 6.4674 | 643.8252 | 0.1421 | 1.1526 |
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+ | 0.3152 | 1.0381 | 220000 | 0.0025 | 0.1298 | 1.1386 | 6.4597 | 638.8783 | 0.1420 | 1.1526 |
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+ | 0.3391 | 1.0477 | 225000 | 0.0025 | 0.1298 | 1.1386 | 6.4528 | 634.4516 | 0.1420 | 1.1526 |
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+ | 0.3319 | 1.0572 | 230000 | 0.0025 | 0.1298 | 1.1386 | 6.4455 | 629.8329 | 0.1420 | 1.1526 |
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+ | 0.3171 | 1.0668 | 235000 | 0.0025 | 0.1298 | 1.1386 | 6.4376 | 624.9101 | 0.1420 | 1.1526 |
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+ | 0.3176 | 1.0763 | 240000 | 0.0025 | 0.1298 | 1.1386 | 6.4300 | 620.1895 | 0.1420 | 1.1526 |
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+ | 0.3101 | 1.0858 | 245000 | 0.0025 | 0.1298 | 1.1386 | 6.4223 | 615.4315 | 0.1420 | 1.1526 |
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+ | 0.3081 | 1.0954 | 250000 | 0.0025 | 0.1298 | 1.1386 | 6.4166 | 611.9308 | 0.1420 | 1.1525 |
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+ | 0.3277 | 1.1049 | 255000 | 0.0025 | 0.1298 | 1.1386 | 6.4105 | 608.1798 | 0.1420 | 1.1525 |
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+ | 0.3083 | 1.1144 | 260000 | 0.0025 | 0.1298 | 1.1386 | 6.4047 | 604.6944 | 0.1419 | 1.1525 |
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+ | 0.3162 | 2.0048 | 265000 | 0.0025 | 0.1298 | 1.1386 | 6.3984 | 600.9119 | 0.1419 | 1.1525 |
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+ | 0.3118 | 2.0143 | 270000 | 0.0025 | 0.1298 | 1.1386 | 6.3924 | 597.3080 | 0.1419 | 1.1525 |
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+ | 0.314 | 2.0238 | 275000 | 0.0025 | 0.1298 | 1.1386 | 6.3858 | 593.3590 | 0.1419 | 1.1525 |
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+ | 0.3149 | 2.0334 | 280000 | 0.0025 | 0.1298 | 1.1386 | 6.3805 | 590.2321 | 0.1419 | 1.1525 |
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+ | 0.3232 | 2.0429 | 285000 | 0.0025 | 0.1298 | 1.1386 | 6.3752 | 587.1187 | 0.1419 | 1.1524 |
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+ | 0.3179 | 2.0525 | 290000 | 0.0025 | 0.1298 | 1.1386 | 6.3707 | 584.4872 | 0.1419 | 1.1524 |
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+ | 0.3149 | 2.0620 | 295000 | 0.0025 | 0.1298 | 1.1386 | 6.3652 | 581.2809 | 0.1419 | 1.1524 |
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+ | 0.3259 | 2.0715 | 300000 | 0.0025 | 0.1298 | 1.1386 | 6.3602 | 578.3447 | 0.1419 | 1.1524 |
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+ | 0.3389 | 2.0811 | 305000 | 0.0025 | 0.1298 | 1.1386 | 6.3549 | 575.3080 | 0.1418 | 1.1524 |
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+ | 0.3119 | 2.0906 | 310000 | 0.0025 | 0.1298 | 1.1386 | 6.3502 | 572.6125 | 0.1418 | 1.1524 |
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+ | 0.3149 | 2.1001 | 315000 | 0.0025 | 0.1298 | 1.1386 | 6.3465 | 570.4726 | 0.1418 | 1.1524 |
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+ | 0.3289 | 2.1097 | 320000 | 0.0025 | 0.1298 | 1.1386 | 6.3422 | 568.0665 | 0.1418 | 1.1524 |
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+ | 0.3111 | 2.1192 | 325000 | 0.0025 | 0.1298 | 1.1386 | 6.3381 | 565.7143 | 0.1418 | 1.1524 |
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+ | 0.3179 | 3.0095 | 330000 | 0.0025 | 0.1298 | 1.1386 | 6.3341 | 563.4519 | 0.1418 | 1.1524 |
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+ | 0.3094 | 3.0191 | 335000 | 0.0025 | 0.1298 | 1.1386 | 6.3298 | 561.0462 | 0.1418 | 1.1523 |
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+ | 0.3396 | 3.0286 | 340000 | 0.0025 | 0.1298 | 1.1386 | 6.3259 | 558.8331 | 0.1418 | 1.1523 |
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+ | 0.314 | 3.0381 | 345000 | 0.0025 | 0.1298 | 1.1386 | 6.3223 | 556.8332 | 0.1418 | 1.1523 |
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+ | 0.3392 | 3.0477 | 350000 | 0.0025 | 0.1298 | 1.1386 | 6.3190 | 555.0390 | 0.1418 | 1.1523 |
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+ | 0.3319 | 3.0572 | 355000 | 0.0025 | 0.1298 | 1.1386 | 6.3157 | 553.2072 | 0.1418 | 1.1523 |
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+ | 0.3169 | 3.0668 | 360000 | 0.0025 | 0.1298 | 1.1386 | 6.3122 | 551.2481 | 0.1418 | 1.1523 |
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+ | 0.3166 | 3.0763 | 365000 | 0.0025 | 0.1298 | 1.1386 | 6.3088 | 549.3597 | 0.1418 | 1.1523 |
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+ | 0.3087 | 3.0858 | 370000 | 0.0025 | 0.1298 | 1.1386 | 6.3053 | 547.4488 | 0.1417 | 1.1523 |
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+ | 0.3074 | 3.0954 | 375000 | 0.0025 | 0.1298 | 1.1386 | 6.3028 | 546.1247 | 0.1417 | 1.1523 |
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+ | 0.3288 | 3.1049 | 380000 | 0.0025 | 0.1298 | 1.1386 | 6.3002 | 544.7043 | 0.1417 | 1.1523 |
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+ | 0.3082 | 3.1144 | 385000 | 0.0025 | 0.1298 | 1.1386 | 6.2977 | 543.3141 | 0.1417 | 1.1523 |
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+ | 0.3171 | 4.0048 | 390000 | 0.0025 | 0.1298 | 1.1386 | 6.2950 | 541.8326 | 0.1417 | 1.1523 |
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+ | 0.3132 | 4.0143 | 395000 | 0.0025 | 0.1298 | 1.1386 | 6.2925 | 540.4888 | 0.1417 | 1.1523 |
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+ | 0.3153 | 4.0238 | 400000 | 0.0025 | 0.1298 | 1.1386 | 6.2896 | 538.9527 | 0.1417 | 1.1523 |
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+ | 0.317 | 4.0334 | 405000 | 0.0025 | 0.1298 | 1.1386 | 6.2874 | 537.7680 | 0.1417 | 1.1522 |
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+ | 0.3161 | 1.0095 | 410000 | 1.1522 | 0.1417 | 0.0025 | 536.6438 | 6.2853 | 1.1386 | 0.1298 |
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+ | 0.31 | 1.0191 | 415000 | 1.1522 | 0.1417 | 0.0025 | 535.4539 | 6.2831 | 1.1386 | 0.1298 |
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+ | 0.3412 | 1.0286 | 420000 | 1.1522 | 0.1417 | 0.0025 | 534.3656 | 6.2811 | 1.1386 | 0.1298 |
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+ | 0.3148 | 1.0381 | 425000 | 1.1522 | 0.1417 | 0.0025 | 533.3932 | 6.2793 | 1.1386 | 0.1298 |
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+ | 0.3387 | 1.0477 | 430000 | 1.1522 | 0.1417 | 0.0025 | 532.5283 | 6.2776 | 1.1386 | 0.1298 |
149
+ | 0.3316 | 1.0572 | 435000 | 1.1522 | 0.1417 | 0.0025 | 531.6586 | 6.2760 | 1.1386 | 0.1298 |
150
+ | 0.3168 | 1.0668 | 440000 | 1.1522 | 0.1417 | 0.0025 | 530.7140 | 6.2742 | 1.1386 | 0.1298 |
151
+ | 0.3173 | 1.0763 | 445000 | 1.1522 | 0.1417 | 0.0025 | 529.8676 | 6.2726 | 1.1386 | 0.1298 |
152
+ | 0.3098 | 1.0858 | 450000 | 1.1522 | 0.1417 | 0.0025 | 529.0170 | 6.2710 | 1.1386 | 0.1298 |
153
+ | 0.3078 | 1.0954 | 455000 | 1.1522 | 0.1417 | 0.0025 | 528.4528 | 6.2700 | 1.1386 | 0.1298 |
154
+ | 0.3274 | 1.1049 | 460000 | 1.1522 | 0.1417 | 0.0025 | 527.8631 | 6.2688 | 1.1386 | 0.1298 |
155
+ | 0.308 | 1.1144 | 465000 | 1.1522 | 0.1417 | 0.0025 | 527.3422 | 6.2678 | 1.1386 | 0.1298 |
156
+ | 0.3159 | 2.0048 | 470000 | 1.1522 | 0.1417 | 0.0025 | 526.7932 | 6.2668 | 1.1386 | 0.1298 |
157
+ | 0.3115 | 2.0143 | 475000 | 1.1522 | 0.1417 | 0.0025 | 526.2932 | 6.2659 | 1.1386 | 0.1298 |
158
+ | 0.3137 | 2.0238 | 480000 | 1.1522 | 0.1417 | 0.0025 | 525.7652 | 6.2649 | 1.1386 | 0.1298 |
159
+ | 0.3146 | 2.0334 | 485000 | 1.1522 | 0.1417 | 0.0025 | 525.3872 | 6.2641 | 1.1386 | 0.1298 |
160
+ | 0.323 | 2.0429 | 490000 | 1.1522 | 0.1417 | 0.0025 | 525.0387 | 6.2635 | 1.1386 | 0.1298 |
161
+ | 0.3177 | 2.0525 | 495000 | 1.1522 | 0.1417 | 0.0025 | 524.7778 | 6.2630 | 1.1386 | 0.1298 |
162
+ | 0.3147 | 2.0620 | 500000 | 1.1522 | 0.1417 | 0.0025 | 524.4927 | 6.2624 | 1.1386 | 0.1298 |
163
+ | 0.3257 | 2.0715 | 505000 | 1.1522 | 0.1417 | 0.0025 | 524.2781 | 6.2620 | 1.1386 | 0.1298 |
164
+ | 0.3387 | 2.0811 | 510000 | 1.1522 | 0.1417 | 0.0025 | 524.1006 | 6.2617 | 1.1386 | 0.1298 |
165
+ | 0.3117 | 2.0906 | 515000 | 1.1522 | 0.1417 | 0.0025 | 523.9845 | 6.2615 | 1.1386 | 0.1298 |
166
+ | 0.3147 | 2.1001 | 520000 | 1.1522 | 0.1417 | 0.0025 | 523.9337 | 6.2614 | 1.1386 | 0.1298 |
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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