End of training

README.md

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 ---
-library_name: transformers
-tags: []
+base_model: Anwaarma/Improved-MARBERT-attempt2
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: unfortified_marbert
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
-
-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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-## How to Get Started with the Model
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+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# unfortified_marbert
+
+This model is a fine-tuned version of [Anwaarma/Improved-MARBERT-attempt2](https://huggingface.co/Anwaarma/Improved-MARBERT-attempt2) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3890
+- Accuracy: 0.92
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| No log        | 0.0546 | 50   | 0.2510          | 0.92     |
+| No log        | 0.1092 | 100  | 0.1780          | 0.94     |
+| No log        | 0.1638 | 150  | 0.3531          | 0.88     |
+| No log        | 0.2183 | 200  | 0.2776          | 0.94     |
+| No log        | 0.2729 | 250  | 0.2577          | 0.94     |
+| No log        | 0.3275 | 300  | 0.2271          | 0.94     |
+| No log        | 0.3821 | 350  | 0.1877          | 0.94     |
+| No log        | 0.4367 | 400  | 0.1124          | 0.96     |
+| No log        | 0.4913 | 450  | 0.3439          | 0.91     |
+| 0.2508        | 0.5459 | 500  | 0.3198          | 0.89     |
+| 0.2508        | 0.6004 | 550  | 0.2230          | 0.92     |
+| 0.2508        | 0.6550 | 600  | 0.2747          | 0.9      |
+| 0.2508        | 0.7096 | 650  | 0.3376          | 0.9      |
+| 0.2508        | 0.7642 | 700  | 0.2156          | 0.93     |
+| 0.2508        | 0.8188 | 750  | 0.3291          | 0.9      |
+| 0.2508        | 0.8734 | 800  | 0.2528          | 0.94     |
+| 0.2508        | 0.9279 | 850  | 0.2131          | 0.92     |
+| 0.2508        | 0.9825 | 900  | 0.2262          | 0.95     |
+| 0.2508        | 1.0371 | 950  | 0.2967          | 0.9      |
+| 0.2238        | 1.0917 | 1000 | 0.2900          | 0.94     |
+| 0.2238        | 1.1463 | 1050 | 0.2720          | 0.92     |
+| 0.2238        | 1.2009 | 1100 | 0.3414          | 0.92     |
+| 0.2238        | 1.2555 | 1150 | 0.2702          | 0.94     |
+| 0.2238        | 1.3100 | 1200 | 0.3387          | 0.93     |
+| 0.2238        | 1.3646 | 1250 | 0.3890          | 0.92     |
+
+
+### Framework versions
+
+- Transformers 4.42.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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