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library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-downstream-indian-ner
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. -->
# roberta-base-downstream-indian-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2666
- Precision: 0.5248
- Recall: 0.7557
- F1: 0.6195
- Accuracy: 0.9547
## 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: 3e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 86 | 0.3551 | 0.0892 | 0.4171 | 0.1469 | 0.7997 |
| No log | 2.0 | 172 | 0.2383 | 0.1328 | 0.4684 | 0.2070 | 0.8327 |
| No log | 3.0 | 258 | 0.2159 | 0.2075 | 0.5253 | 0.2975 | 0.8922 |
| No log | 4.0 | 344 | 0.2013 | 0.2338 | 0.5344 | 0.3253 | 0.9025 |
| No log | 5.0 | 430 | 0.1926 | 0.2732 | 0.5476 | 0.3646 | 0.9131 |
| 0.396 | 6.0 | 516 | 0.2002 | 0.2821 | 0.5717 | 0.3778 | 0.9134 |
| 0.396 | 7.0 | 602 | 0.2103 | 0.3407 | 0.6220 | 0.4403 | 0.9267 |
| 0.396 | 8.0 | 688 | 0.1944 | 0.3388 | 0.6265 | 0.4398 | 0.9256 |
| 0.396 | 9.0 | 774 | 0.2118 | 0.3477 | 0.6349 | 0.4494 | 0.9291 |
| 0.396 | 10.0 | 860 | 0.2274 | 0.4096 | 0.6729 | 0.5092 | 0.9396 |
| 0.396 | 11.0 | 946 | 0.2318 | 0.4527 | 0.7047 | 0.5513 | 0.9450 |
| 0.0715 | 12.0 | 1032 | 0.2439 | 0.4436 | 0.6946 | 0.5414 | 0.9443 |
| 0.0715 | 13.0 | 1118 | 0.2385 | 0.4781 | 0.7379 | 0.5802 | 0.9460 |
| 0.0715 | 14.0 | 1204 | 0.2420 | 0.4584 | 0.7065 | 0.5560 | 0.9460 |
| 0.0715 | 15.0 | 1290 | 0.2455 | 0.4992 | 0.7344 | 0.5944 | 0.9502 |
| 0.0715 | 16.0 | 1376 | 0.2513 | 0.5377 | 0.7644 | 0.6313 | 0.9572 |
| 0.0715 | 17.0 | 1462 | 0.2670 | 0.5354 | 0.7627 | 0.6291 | 0.9558 |
| 0.0344 | 18.0 | 1548 | 0.2687 | 0.5020 | 0.7351 | 0.5966 | 0.9505 |
| 0.0344 | 19.0 | 1634 | 0.2666 | 0.5248 | 0.7557 | 0.6195 | 0.9547 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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