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license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-finetuned-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-finetuned-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: 0.1424
- Precision: 0.9657
- Recall: 0.9608
- F1: 0.9633
- Accuracy: 0.9594
## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1562 | 0.25 | 7000 | 0.1457 | 0.9547 | 0.9376 | 0.9460 | 0.9407 |
| 0.1375 | 0.5 | 14000 | 0.1472 | 0.9569 | 0.9414 | 0.9491 | 0.9442 |
| 0.1371 | 0.75 | 21000 | 0.1331 | 0.9570 | 0.9480 | 0.9524 | 0.9482 |
| 0.1325 | 0.99 | 28000 | 0.1216 | 0.9603 | 0.9487 | 0.9545 | 0.9501 |
| 0.1176 | 1.24 | 35000 | 0.1307 | 0.9617 | 0.9468 | 0.9542 | 0.9496 |
| 0.1191 | 1.49 | 42000 | 0.1230 | 0.9596 | 0.9521 | 0.9558 | 0.9516 |
| 0.116 | 1.74 | 49000 | 0.1341 | 0.9634 | 0.9510 | 0.9572 | 0.9528 |
| 0.1136 | 1.99 | 56000 | 0.1179 | 0.9582 | 0.9560 | 0.9571 | 0.9527 |
| 0.0947 | 2.24 | 63000 | 0.1426 | 0.9560 | 0.9544 | 0.9552 | 0.9512 |
| 0.1009 | 2.49 | 70000 | 0.1155 | 0.9644 | 0.9549 | 0.9596 | 0.9556 |
| 0.0972 | 2.73 | 77000 | 0.1282 | 0.9654 | 0.9543 | 0.9598 | 0.9556 |
| 0.0959 | 2.98 | 84000 | 0.1216 | 0.9642 | 0.9566 | 0.9603 | 0.9564 |
| 0.0847 | 3.23 | 91000 | 0.1222 | 0.9645 | 0.9580 | 0.9612 | 0.9573 |
| 0.0817 | 3.48 | 98000 | 0.1275 | 0.9648 | 0.9575 | 0.9611 | 0.9571 |
| 0.089 | 3.73 | 105000 | 0.1260 | 0.9661 | 0.9577 | 0.9619 | 0.9580 |
| 0.0826 | 3.98 | 112000 | 0.1188 | 0.9641 | 0.9593 | 0.9617 | 0.9577 |
| 0.072 | 4.23 | 119000 | 0.1361 | 0.9645 | 0.9598 | 0.9621 | 0.9581 |
| 0.0648 | 4.47 | 126000 | 0.1377 | 0.9640 | 0.9601 | 0.9620 | 0.9581 |
| 0.0665 | 4.72 | 133000 | 0.1352 | 0.9655 | 0.9596 | 0.9625 | 0.9586 |
| 0.0692 | 4.97 | 140000 | 0.1392 | 0.9668 | 0.9593 | 0.9631 | 0.9593 |
| 0.0545 | 5.22 | 147000 | 0.1470 | 0.9663 | 0.9602 | 0.9633 | 0.9595 |
| 0.0549 | 5.47 | 154000 | 0.1442 | 0.9652 | 0.9611 | 0.9631 | 0.9593 |
| 0.0563 | 5.72 | 161000 | 0.1454 | 0.9657 | 0.9609 | 0.9633 | 0.9594 |
| 0.0647 | 5.97 | 168000 | 0.1424 | 0.9657 | 0.9608 | 0.9633 | 0.9594 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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