README.md
| 1 | --- |
| 2 | license: mit |
| 3 | base_model: xlm-roberta-base |
| 4 | tags: |
| 5 | - generated_from_trainer |
| 6 | metrics: |
| 7 | - f1 |
| 8 | model-index: |
| 9 | - name: Bangla-Twoclass-Sentiment-Analyzer |
| 10 | results: [] |
| 11 | --- |
| 12 | |
| 13 | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| 14 | should probably proofread and complete it, then remove this comment. --> |
| 15 | |
| 16 | # Bangla-Twoclass-Sentiment-Analyzer |
| 17 | |
| 18 | This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
| 19 | It achieves the following results on the evaluation set: |
| 20 | - Loss: 2.7755 |
| 21 | - F1: 0.6113 |
| 22 | |
| 23 | ## Model description |
| 24 | |
| 25 | More information needed |
| 26 | |
| 27 | ## Intended uses & limitations |
| 28 | |
| 29 | More information needed |
| 30 | |
| 31 | ## Training and evaluation data |
| 32 | |
| 33 | More information needed |
| 34 | |
| 35 | ## Training procedure |
| 36 | |
| 37 | ### Training hyperparameters |
| 38 | |
| 39 | The following hyperparameters were used during training: |
| 40 | - learning_rate: 3e-05 |
| 41 | - train_batch_size: 16 |
| 42 | - eval_batch_size: 16 |
| 43 | - seed: 42 |
| 44 | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| 45 | - lr_scheduler_type: linear |
| 46 | - training_steps: 1800 |
| 47 | - mixed_precision_training: Native AMP |
| 48 | |
| 49 | ### Training results |
| 50 | |
| 51 | | Training Loss | Epoch | Step | Validation Loss | F1 | |
| 52 | |:-------------:|:-----:|:----:|:---------------:|:------:| |
| 53 | | No log | 2.53 | 200 | 0.9869 | 0.4635 | |
| 54 | | No log | 5.06 | 400 | 0.8978 | 0.5858 | |
| 55 | | 0.8692 | 7.59 | 600 | 1.1978 | 0.6149 | |
| 56 | | 0.8692 | 10.13 | 800 | 1.5145 | 0.6112 | |
| 57 | | 0.3138 | 12.66 | 1000 | 2.0353 | 0.6041 | |
| 58 | | 0.3138 | 15.19 | 1200 | 2.4316 | 0.6203 | |
| 59 | | 0.3138 | 17.72 | 1400 | 2.6025 | 0.6002 | |
| 60 | | 0.0769 | 20.25 | 1600 | 2.6247 | 0.6082 | |
| 61 | | 0.0769 | 22.78 | 1800 | 2.7755 | 0.6113 | |
| 62 | |
| 63 | |
| 64 | ### Framework versions |
| 65 | |
| 66 | - Transformers 4.37.2 |
| 67 | - Pytorch 2.1.0+cu121 |
| 68 | - Datasets 2.17.1 |
| 69 | - Tokenizers 0.15.2 |
| 70 | |