| | --- |
| | 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 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.44.2 |
| | - Pytorch 2.4.0+cu121 |
| | - Datasets 2.21.0 |
| | - Tokenizers 0.19.1 |
| |
|
