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--- |
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license: apache-2.0 |
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base_model: bert-base-cased |
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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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- f1 |
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- accuracy |
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model-index: |
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- name: assignment2_meher_test2 |
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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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# assignment2_meher_test2 |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5440 |
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- Precision: 0.2070 |
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- Recall: 0.2440 |
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- F1: 0.2240 |
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- Accuracy: 0.9244 |
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## Model description |
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More information needed |
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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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## 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: 8 |
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- eval_batch_size: 8 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 347 | 0.2833 | 0.1672 | 0.1787 | 0.1728 | 0.9252 | |
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| 0.2912 | 2.0 | 694 | 0.3104 | 0.1923 | 0.2062 | 0.1990 | 0.9262 | |
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| 0.1166 | 3.0 | 1041 | 0.3258 | 0.1973 | 0.2474 | 0.2195 | 0.9235 | |
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| 0.1166 | 4.0 | 1388 | 0.3608 | 0.1818 | 0.3024 | 0.2271 | 0.9131 | |
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| 0.054 | 5.0 | 1735 | 0.4753 | 0.2093 | 0.2165 | 0.2128 | 0.9239 | |
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| 0.0277 | 6.0 | 2082 | 0.4959 | 0.2181 | 0.2405 | 0.2288 | 0.9246 | |
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| 0.0277 | 7.0 | 2429 | 0.5534 | 0.2331 | 0.1890 | 0.2087 | 0.9309 | |
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| 0.0159 | 8.0 | 2776 | 0.5215 | 0.2281 | 0.2509 | 0.2390 | 0.9254 | |
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| 0.0091 | 9.0 | 3123 | 0.5522 | 0.2244 | 0.2405 | 0.2322 | 0.9256 | |
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| 0.0091 | 10.0 | 3470 | 0.5440 | 0.2070 | 0.2440 | 0.2240 | 0.9244 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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