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  4. training_args.bin +3 -0
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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- ## Model Details
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- ### Model Description
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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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-
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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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-
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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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- #### 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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- ## Evaluation
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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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- 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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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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  ---
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+ license: mit
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+ base_model: facebook/esm2_t6_8M_UR50D
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ library_name: peft
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+ model-index:
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+ - name: esm_ft_Aerin_Yang_et_al_2023
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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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+ # esm_ft_Aerin_Yang_et_al_2023
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+
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+ This model is a fine-tuned version of [facebook/esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1828
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+ - Rmse: 1.3057
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+ - Mae: 0.9676
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+ - Spearmanr Corr: 0.8519
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+ - Spearmanr Corr P Value: 0.0000
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+ - Pearsonr Corr: 0.8936
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+ - Pearsonr Corr P Value: 0.0000
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+ - Spearmanr Corr Of Deltas: 0.8712
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+ - Spearmanr Corr Of Deltas P Value: 0.0
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+ - Pearsonr Corr Of Deltas: 0.8932
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+ - Pearsonr Corr Of Deltas P Value: 0.0
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+ - Ranking F1 Score: 0.7640
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+ - Ranking Mcc: 0.6329
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+ - Rmse Enriched: 0.3328
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+ - Mae Enriched: 0.1009
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+ - Spearmanr Corr Enriched: -0.0212
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+ - Spearmanr Corr Enriched P Value: 0.7120
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+ - Pearsonr Corr Enriched: 0.0168
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+ - Pearsonr Corr Enriched P Value: 0.7695
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+ - Spearmanr Corr Of Deltas Enriched: -0.0213
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+ - Spearmanr Corr Of Deltas Enriched P Value: 0.0000
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+ - Pearsonr Corr Of Deltas Enriched: 0.0105
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+ - Pearsonr Corr Of Deltas Enriched P Value: 0.0238
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+ - Ranking F1 Score Enriched: 0.0070
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+ - Ranking Mcc Enriched: -0.0067
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+ - Classification Thresh: 0.5
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+ - Mcc: 0.8893
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+ - F1 Score: 0.9402
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+ - Acc: 0.9448
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+ - Auc: 0.9781
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+ - Precision: 0.9437
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+ - Recall: 0.9455
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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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+ More information needed
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+ ## Training and evaluation data
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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: 0.001
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - distributed_type: not_parallel
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 200
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+ - num_epochs: 100
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Rmse | Mae | Spearmanr Corr | Spearmanr Corr P Value | Pearsonr Corr | Pearsonr Corr P Value | Spearmanr Corr Of Deltas | Spearmanr Corr Of Deltas P Value | Pearsonr Corr Of Deltas | Pearsonr Corr Of Deltas P Value | Ranking F1 Score | Ranking Mcc | Rmse Enriched | Mae Enriched | Spearmanr Corr Enriched | Spearmanr Corr Enriched P Value | Pearsonr Corr Enriched | Pearsonr Corr Enriched P Value | Spearmanr Corr Of Deltas Enriched | Spearmanr Corr Of Deltas Enriched P Value | Pearsonr Corr Of Deltas Enriched | Pearsonr Corr Of Deltas Enriched P Value | Ranking F1 Score Enriched | Ranking Mcc Enriched | Classification Thresh | Mcc | F1 Score | Acc | Auc | Precision | Recall |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:--------------:|:----------------------:|:-------------:|:---------------------:|:------------------------:|:--------------------------------:|:-----------------------:|:-------------------------------:|:----------------:|:-----------:|:-------------:|:------------:|:-----------------------:|:-------------------------------:|:----------------------:|:------------------------------:|:---------------------------------:|:-----------------------------------------:|:--------------------------------:|:----------------------------------------:|:-------------------------:|:--------------------:|:---------------------:|:------:|:--------:|:------:|:------:|:---------:|:------:|
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+ | 0.4744 | 1.0 | 335 | 0.2204 | 1.2146 | 0.9287 | 0.8345 | 0.0000 | 0.8644 | 0.0000 | 0.8427 | 0.0 | 0.8638 | 0.0 | 0.7535 | 0.6154 | 0.5256 | 0.3052 | -0.0491 | 0.3924 | -0.0107 | 0.8518 | -0.0443 | 0.0000 | -0.0158 | 0.0007 | 0.0048 | -0.0216 | 0.01 | 0.8352 | 0.9066 | 0.9179 | 0.9702 | 0.9208 | 0.9144 |
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+ | 0.2856 | 2.0 | 670 | 0.2159 | 1.3143 | 0.9756 | 0.8301 | 0.0000 | 0.8757 | 0.0000 | 0.8501 | 0.0 | 0.8752 | 0.0 | 0.7458 | 0.6026 | 0.4886 | 0.1839 | -0.0148 | 0.7973 | 0.0253 | 0.6600 | -0.0072 | 0.1225 | 0.0231 | 0.0000 | 0.0063 | -0.0116 | 0.3 | 0.8648 | 0.9246 | 0.9328 | 0.9744 | 0.9342 | 0.9305 |
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+ | 0.2415 | 3.0 | 1005 | 0.1751 | 1.2217 | 0.9352 | 0.8437 | 0.0000 | 0.8977 | 0.0000 | 0.8697 | 0.0 | 0.8973 | 0.0 | 0.7555 | 0.6187 | 0.3297 | 0.1524 | -0.0411 | 0.4749 | 0.0159 | 0.7818 | -0.0365 | 0.0000 | 0.0095 | 0.0405 | 0.0050 | -0.0204 | 0.3 | 0.8918 | 0.9414 | 0.9463 | 0.9770 | 0.9455 | 0.9464 |
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+ | 0.2345 | 4.0 | 1340 | 0.1869 | 1.2521 | 0.9313 | 0.8469 | 0.0000 | 0.8859 | 0.0000 | 0.8680 | 0.0 | 0.8854 | 0.0 | 0.7606 | 0.6271 | 0.4073 | 0.1542 | 0.0233 | 0.6852 | 0.0240 | 0.6766 | 0.0288 | 0.0000 | 0.0182 | 0.0001 | 0.0087 | 0.0053 | 0.3 | 0.8766 | 0.9327 | 0.9388 | 0.9786 | 0.9384 | 0.9382 |
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+ | 0.2242 | 5.0 | 1675 | 0.1761 | 1.2067 | 0.9313 | 0.8304 | 0.0000 | 0.8968 | 0.0000 | 0.8607 | 0.0 | 0.8963 | 0.0 | 0.7412 | 0.5947 | 0.3551 | 0.1941 | -0.0104 | 0.8567 | 0.0159 | 0.7822 | -0.0139 | 0.0027 | 0.0090 | 0.0532 | 0.0079 | -0.0006 | 0.5 | 0.8947 | 0.9427 | 0.9478 | 0.9766 | 0.9472 | 0.9475 |
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+ | 0.1845 | 6.0 | 2010 | 0.1655 | 1.2685 | 0.9459 | 0.8486 | 0.0000 | 0.8996 | 0.0000 | 0.8750 | 0.0 | 0.8991 | 0.0 | 0.7615 | 0.6288 | 0.4082 | 0.1647 | 0.0078 | 0.8915 | 0.0235 | 0.6830 | 0.0096 | 0.0379 | 0.0186 | 0.0001 | 0.0085 | 0.0040 | 0.2 | 0.8886 | 0.9388 | 0.9448 | 0.9782 | 0.9450 | 0.9437 |
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+ | 0.1933 | 7.0 | 2345 | 0.1999 | 1.3220 | 0.9762 | 0.8338 | 0.0000 | 0.8857 | 0.0000 | 0.8617 | 0.0 | 0.8852 | 0.0 | 0.7503 | 0.6101 | 0.4461 | 0.1626 | -0.0051 | 0.9297 | 0.0235 | 0.6828 | 0.0007 | 0.8758 | 0.0173 | 0.0002 | 0.0076 | -0.0024 | 0.1 | 0.8826 | 0.9360 | 0.9418 | 0.9707 | 0.9414 | 0.9412 |
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+ | 0.2323 | 8.0 | 2680 | 0.1912 | 1.2274 | 0.9298 | 0.8362 | 0.0000 | 0.8846 | 0.0000 | 0.8562 | 0.0 | 0.8841 | 0.0 | 0.7461 | 0.6030 | 0.5002 | 0.2814 | 0.0501 | 0.3836 | 0.0323 | 0.5738 | 0.0373 | 0.0000 | 0.0269 | 0.0000 | 0.0119 | 0.0278 | 0.1 | 0.8710 | 0.9277 | 0.9358 | 0.9795 | 0.9377 | 0.9333 |
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+ | 0.1706 | 9.0 | 3015 | 0.3637 | 1.1873 | 0.9135 | 0.8473 | 0.0000 | 0.7821 | 0.0000 | 0.8159 | 0.0 | 0.7810 | 0.0 | 0.7607 | 0.6274 | 0.8092 | 0.5524 | -0.0164 | 0.7757 | 0.0404 | 0.4826 | 0.0031 | 0.5037 | 0.0345 | 0.0000 | 0.0064 | -0.0109 | 0.1 | 0.7485 | 0.8404 | 0.8701 | 0.9740 | 0.8887 | 0.8603 |
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+ | 0.2132 | 10.0 | 3350 | 0.1819 | 1.2570 | 0.9619 | 0.8476 | 0.0000 | 0.8988 | 0.0000 | 0.8735 | 0.0 | 0.8983 | 0.0 | 0.7590 | 0.6247 | 0.2774 | 0.1046 | 0.0330 | 0.5660 | 0.0259 | 0.6517 | 0.0270 | 0.0000 | 0.0199 | 0.0000 | 0.0102 | 0.0153 | 0.01 | 0.8893 | 0.9402 | 0.9448 | 0.9811 | 0.9437 | 0.9455 |
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+ | 0.1832 | 11.0 | 3685 | 0.1828 | 1.3057 | 0.9676 | 0.8519 | 0.0000 | 0.8936 | 0.0000 | 0.8712 | 0.0 | 0.8932 | 0.0 | 0.7640 | 0.6329 | 0.3328 | 0.1009 | -0.0212 | 0.7120 | 0.0168 | 0.7695 | -0.0213 | 0.0000 | 0.0105 | 0.0238 | 0.0070 | -0.0067 | 0.5 | 0.8893 | 0.9402 | 0.9448 | 0.9781 | 0.9437 | 0.9455 |
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+ ### Framework versions
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+
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+ - PEFT 0.13.2
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+ - Transformers 4.41.2
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+ - Pytorch 2.5.1+cu118
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+ - Datasets 3.1.0
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+ - Tokenizers 0.19.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ "task_type": "SEQ_CLS"
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+ }
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