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update model card README.md
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README.md
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---
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license: mit
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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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- precision
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- recall
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- f1
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model-index:
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- name: roberta-large-finetuned-code-mixed-DS
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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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# roberta-large-finetuned-code-mixed-DS
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.0032
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- Accuracy: 0.7344
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- Precision: 0.6701
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- Recall: 0.6682
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- F1: 0.6688
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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: 1e-05
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- train_batch_size: 16
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- eval_batch_size: 32
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- seed: 43
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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 | Accuracy | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| 0.9813 | 1.0 | 248 | 0.8133 | 0.6197 | 0.5679 | 0.5797 | 0.5139 |
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| 0.7795 | 1.99 | 496 | 0.7135 | 0.7183 | 0.6739 | 0.6963 | 0.6782 |
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| 0.6188 | 2.99 | 744 | 0.7418 | 0.7324 | 0.6726 | 0.6860 | 0.6761 |
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| 0.4741 | 3.98 | 992 | 0.8716 | 0.7123 | 0.6495 | 0.6615 | 0.6501 |
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| 0.3326 | 4.98 | 1240 | 1.1437 | 0.7163 | 0.6502 | 0.6470 | 0.6475 |
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| 0.2636 | 5.98 | 1488 | 1.3626 | 0.7264 | 0.6832 | 0.6583 | 0.6587 |
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| 0.1783 | 6.97 | 1736 | 1.5554 | 0.7445 | 0.6958 | 0.6833 | 0.6823 |
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| 0.1258 | 7.97 | 1984 | 1.6650 | 0.7404 | 0.6773 | 0.6731 | 0.6747 |
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| 0.099 | 8.96 | 2232 | 1.8831 | 0.7304 | 0.6637 | 0.6622 | 0.6627 |
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| 0.0932 | 9.96 | 2480 | 2.0032 | 0.7344 | 0.6701 | 0.6682 | 0.6688 |
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### Framework versions
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- Transformers 4.20.1
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- Pytorch 1.10.1+cu111
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- Datasets 2.3.2
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- Tokenizers 0.12.1
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