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---
library_name: transformers
license: mit
base_model: ai4bharat/indic-bert
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: indic-bert-abusive-comments-ta
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. -->
# indic-bert-abusive-comments-ta
This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1108
- Accuracy: 0.6896
- Precision: 0.2983
- Recall: 0.2872
- F1: 0.2740
## 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: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.1508 | 1.0 | 93 | 1.2143 | 0.6216 | 0.1373 | 0.1440 | 0.1253 |
| 1.1999 | 2.0 | 186 | 1.1389 | 0.6337 | 0.1632 | 0.1656 | 0.1545 |
| 1.0445 | 3.0 | 279 | 1.0623 | 0.6815 | 0.2057 | 0.2219 | 0.2109 |
| 0.882 | 4.0 | 372 | 1.0181 | 0.6996 | 0.3264 | 0.3092 | 0.2976 |
| 0.7892 | 5.0 | 465 | 1.0408 | 0.7036 | 0.3327 | 0.3097 | 0.3011 |
| 0.7381 | 6.0 | 558 | 1.1007 | 0.7103 | 0.3317 | 0.3060 | 0.3057 |
| 0.6175 | 7.0 | 651 | 1.2323 | 0.7070 | 0.3241 | 0.3014 | 0.3037 |
| 0.49 | 8.0 | 744 | 1.2686 | 0.6956 | 0.4590 | 0.3093 | 0.3164 |
| 0.4576 | 9.0 | 837 | 1.3347 | 0.6761 | 0.3285 | 0.3200 | 0.3199 |
| 0.3436 | 10.0 | 930 | 1.3501 | 0.6673 | 0.3363 | 0.3167 | 0.3204 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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