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---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: HODravidianLangTech
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. -->
# HODravidianLangTech
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5859
- F1: 0.6908
## 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: 1e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 1234
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 100 | 0.6926 | 0.3377 |
| No log | 2.0 | 200 | 0.6916 | 0.5490 |
| No log | 3.0 | 300 | 0.6856 | 0.6050 |
| No log | 4.0 | 400 | 0.6701 | 0.6287 |
| 0.6833 | 5.0 | 500 | 0.6601 | 0.6396 |
| 0.6833 | 6.0 | 600 | 0.6511 | 0.6466 |
| 0.6833 | 7.0 | 700 | 0.6447 | 0.6458 |
| 0.6833 | 8.0 | 800 | 0.6250 | 0.6560 |
| 0.6833 | 9.0 | 900 | 0.6113 | 0.6516 |
| 0.624 | 10.0 | 1000 | 0.6051 | 0.6658 |
| 0.624 | 11.0 | 1100 | 0.6075 | 0.6567 |
| 0.624 | 12.0 | 1200 | 0.6038 | 0.6671 |
| 0.624 | 13.0 | 1300 | 0.5997 | 0.6716 |
| 0.624 | 14.0 | 1400 | 0.5949 | 0.6805 |
| 0.5739 | 15.0 | 1500 | 0.5958 | 0.6885 |
| 0.5739 | 16.0 | 1600 | 0.5924 | 0.6905 |
| 0.5739 | 17.0 | 1700 | 0.5957 | 0.6875 |
| 0.5739 | 18.0 | 1800 | 0.5839 | 0.6976 |
| 0.5739 | 19.0 | 1900 | 0.5865 | 0.6908 |
| 0.5598 | 20.0 | 2000 | 0.5859 | 0.6908 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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