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---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
base_model: l3cube-pune/hing-roberta
model-index:
- name: hing-roberta-ours-run-5
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. -->
# hing-roberta-ours-run-5
This model is a fine-tuned version of [l3cube-pune/hing-roberta](https://huggingface.co/l3cube-pune/hing-roberta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0980
- Accuracy: 0.725
- Precision: 0.6881
- Recall: 0.6575
- F1: 0.6651
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9336 | 1.0 | 200 | 0.7394 | 0.675 | 0.6450 | 0.6509 | 0.6398 |
| 0.6924 | 2.0 | 400 | 0.9530 | 0.66 | 0.6285 | 0.5845 | 0.5551 |
| 0.4406 | 3.0 | 600 | 0.8914 | 0.68 | 0.6462 | 0.6527 | 0.6479 |
| 0.2493 | 4.0 | 800 | 1.7083 | 0.68 | 0.6441 | 0.6446 | 0.6426 |
| 0.1231 | 5.0 | 1000 | 1.9496 | 0.695 | 0.6570 | 0.6624 | 0.6591 |
| 0.0788 | 6.0 | 1200 | 2.5025 | 0.67 | 0.6209 | 0.6039 | 0.6011 |
| 0.0408 | 7.0 | 1400 | 2.2651 | 0.695 | 0.6594 | 0.6617 | 0.6517 |
| 0.0434 | 8.0 | 1600 | 2.4072 | 0.725 | 0.6941 | 0.6754 | 0.6710 |
| 0.0074 | 9.0 | 1800 | 2.7817 | 0.7 | 0.6535 | 0.6467 | 0.6488 |
| 0.023 | 10.0 | 2000 | 2.8578 | 0.7 | 0.6470 | 0.6353 | 0.6337 |
| 0.0151 | 11.0 | 2200 | 2.7783 | 0.695 | 0.6457 | 0.6373 | 0.6390 |
| 0.0108 | 12.0 | 2400 | 2.5953 | 0.695 | 0.6563 | 0.6586 | 0.6564 |
| 0.0192 | 13.0 | 2600 | 3.0715 | 0.705 | 0.6631 | 0.6326 | 0.6320 |
| 0.0149 | 14.0 | 2800 | 3.1048 | 0.715 | 0.6769 | 0.6450 | 0.6503 |
| 0.0205 | 15.0 | 3000 | 2.7812 | 0.71 | 0.6657 | 0.6538 | 0.6565 |
| 0.0024 | 16.0 | 3200 | 2.9304 | 0.72 | 0.6796 | 0.6537 | 0.6610 |
| 0.0033 | 17.0 | 3400 | 2.7170 | 0.73 | 0.6899 | 0.6760 | 0.6811 |
| 0.0056 | 18.0 | 3600 | 2.9693 | 0.72 | 0.6783 | 0.6560 | 0.6628 |
| 0.0015 | 19.0 | 3800 | 3.0943 | 0.72 | 0.6825 | 0.6541 | 0.6611 |
| 0.0017 | 20.0 | 4000 | 3.0980 | 0.725 | 0.6881 | 0.6575 | 0.6651 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Tokenizers 0.13.2
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