End of training

README.md

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+---
+license: mit
+base_model: sagorsarker/bangla-bert-base
+tags:
+- generated_from_trainer
+metrics:
+- f1
+- accuracy
+model-index:
+- name: bangla-bert-base-MLTC-1
+  results: []
+---
+
+<!-- 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. -->
+
+# bangla-bert-base-MLTC-1
+
+This model is a fine-tuned version of [sagorsarker/bangla-bert-base](https://huggingface.co/sagorsarker/bangla-bert-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3665
+- F1: 0.8480
+- Roc Auc: 0.8444
+- Accuracy: 0.5501
+- Hamming Loss: 0.1555
+- Jaccard Score: 0.7361
+- Zero One Loss: 0.4499
+
+## 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy | Hamming Loss | Jaccard Score | Zero One Loss |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|:------------:|:-------------:|:-------------:|
+| 0.4139        | 1.0   | 73   | 0.4139          | 0.8256 | 0.8213  | 0.5064   | 0.1787       | 0.7030        | 0.4936        |
+| 0.3552        | 2.0   | 146  | 0.3682          | 0.8533 | 0.8522  | 0.5681   | 0.1478       | 0.7442        | 0.4319        |
+| 0.2873        | 3.0   | 219  | 0.3640          | 0.8491 | 0.8451  | 0.5578   | 0.1549       | 0.7378        | 0.4422        |
+| 0.2034        | 4.0   | 292  | 0.3644          | 0.8489 | 0.8451  | 0.5527   | 0.1549       | 0.7375        | 0.4473        |
+| 0.1976        | 5.0   | 365  | 0.3665          | 0.8480 | 0.8444  | 0.5501   | 0.1555       | 0.7361        | 0.4499        |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.1.2
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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