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robust-mbert

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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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- # Model Card for Model ID
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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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- ## Uses
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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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- ## Evaluation
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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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  ---
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+ base_model: Anwaarma/Improved-mBERT-attempt2
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: robust-mbert
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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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+ # robust-mbert
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+
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+ This model is a fine-tuned version of [Anwaarma/Improved-mBERT-attempt2](https://huggingface.co/Anwaarma/Improved-mBERT-attempt2) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2410
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+ - Accuracy: 0.92
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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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+ More information needed
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+ ## Training procedure
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+ ### Training hyperparameters
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-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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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 10
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+
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+ ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:------:|:----:|:---------------:|:--------:|
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+ | No log | 0.0546 | 50 | 0.3348 | 0.88 |
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+ | No log | 0.1092 | 100 | 0.3140 | 0.89 |
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+ | No log | 0.1638 | 150 | 0.4226 | 0.84 |
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+ | No log | 0.2183 | 200 | 0.2552 | 0.92 |
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+ | No log | 0.2729 | 250 | 0.3494 | 0.85 |
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+ | No log | 0.3275 | 300 | 0.2387 | 0.94 |
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+ | No log | 0.3821 | 350 | 0.3383 | 0.87 |
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+ | No log | 0.4367 | 400 | 0.3088 | 0.9 |
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+ | No log | 0.4913 | 450 | 0.3561 | 0.89 |
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+ | 0.3057 | 0.5459 | 500 | 0.3598 | 0.85 |
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+ | 0.3057 | 0.6004 | 550 | 0.2880 | 0.89 |
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+ | 0.3057 | 0.6550 | 600 | 0.2306 | 0.92 |
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+ | 0.3057 | 0.7096 | 650 | 0.3648 | 0.88 |
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+ | 0.3057 | 0.7642 | 700 | 0.2796 | 0.9 |
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+ | 0.3057 | 0.8188 | 750 | 0.3100 | 0.88 |
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+ | 0.3057 | 0.8734 | 800 | 0.2689 | 0.91 |
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+ | 0.3057 | 0.9279 | 850 | 0.2707 | 0.89 |
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+ | 0.3057 | 0.9825 | 900 | 0.2684 | 0.87 |
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+ | 0.3057 | 1.0371 | 950 | 0.4417 | 0.86 |
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+ | 0.2777 | 1.0917 | 1000 | 0.3980 | 0.88 |
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+ | 0.2777 | 1.1463 | 1050 | 0.3233 | 0.9 |
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+ | 0.2777 | 1.2009 | 1100 | 0.2857 | 0.9 |
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+ | 0.2777 | 1.2555 | 1150 | 0.3229 | 0.89 |
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+ | 0.2777 | 1.3100 | 1200 | 0.2364 | 0.92 |
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+ | 0.2777 | 1.3646 | 1250 | 0.3015 | 0.87 |
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+ | 0.2777 | 1.4192 | 1300 | 0.2713 | 0.89 |
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+ | 0.2777 | 1.4738 | 1350 | 0.3839 | 0.87 |
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+ | 0.2777 | 1.5284 | 1400 | 0.3173 | 0.9 |
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+ | 0.2777 | 1.5830 | 1450 | 0.2690 | 0.91 |
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+ | 0.2138 | 1.6376 | 1500 | 0.3804 | 0.89 |
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+ | 0.2138 | 1.6921 | 1550 | 0.3020 | 0.88 |
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+ | 0.2138 | 1.7467 | 1600 | 0.2702 | 0.89 |
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+ | 0.2138 | 1.8013 | 1650 | 0.2815 | 0.9 |
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+ | 0.2138 | 1.8559 | 1700 | 0.2867 | 0.89 |
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+ | 0.2138 | 1.9105 | 1750 | 0.2861 | 0.87 |
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+ | 0.2138 | 1.9651 | 1800 | 0.2585 | 0.89 |
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+ | 0.2138 | 2.0197 | 1850 | 0.3170 | 0.9 |
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+ | 0.2138 | 2.0742 | 1900 | 0.2928 | 0.9 |
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+ | 0.2138 | 2.1288 | 1950 | 0.2635 | 0.93 |
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+ | 0.1966 | 2.1834 | 2000 | 0.2695 | 0.93 |
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+ | 0.1966 | 2.2380 | 2050 | 0.3348 | 0.9 |
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+ | 0.1966 | 2.2926 | 2100 | 0.3577 | 0.91 |
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+ | 0.1966 | 2.3472 | 2150 | 0.3360 | 0.92 |
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+ | 0.1966 | 2.4017 | 2200 | 0.3721 | 0.91 |
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+ | 0.1966 | 2.4563 | 2250 | 0.2410 | 0.92 |
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+ ### Framework versions
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+ - Transformers 4.42.2
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+ - Pytorch 2.3.0+cu121
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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