roberta-base-downstream-indian-ner
This model is a fine-tuned version of 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
