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---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RoBERTa_conll_epoch_10
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9443059019118869
- name: Recall
type: recall
value: 0.9559071019858634
- name: F1
type: f1
value: 0.9500710880655683
- name: Accuracy
type: accuracy
value: 0.9882329477463103
---
<!-- 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_conll_epoch_10
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0906
- Precision: 0.9443
- Recall: 0.9559
- F1: 0.9501
- Accuracy: 0.9882
## 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: 8
- eval_batch_size: 8
- seed: 42
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0839 | 1.0 | 1756 | 0.0705 | 0.9055 | 0.9303 | 0.9177 | 0.9827 |
| 0.0454 | 2.0 | 3512 | 0.0690 | 0.9257 | 0.9431 | 0.9343 | 0.9853 |
| 0.0272 | 3.0 | 5268 | 0.0590 | 0.9310 | 0.9495 | 0.9402 | 0.9865 |
| 0.0183 | 4.0 | 7024 | 0.0803 | 0.9324 | 0.9515 | 0.9419 | 0.9862 |
| 0.0129 | 5.0 | 8780 | 0.0747 | 0.9433 | 0.9517 | 0.9475 | 0.9872 |
| 0.0079 | 6.0 | 10536 | 0.0792 | 0.9359 | 0.9534 | 0.9446 | 0.9874 |
| 0.0055 | 7.0 | 12292 | 0.0785 | 0.9457 | 0.9549 | 0.9503 | 0.9879 |
| 0.003 | 8.0 | 14048 | 0.0881 | 0.9438 | 0.9561 | 0.9499 | 0.9879 |
| 0.001 | 9.0 | 15804 | 0.0875 | 0.9448 | 0.9562 | 0.9505 | 0.9879 |
| 0.0008 | 10.0 | 17560 | 0.0906 | 0.9443 | 0.9559 | 0.9501 | 0.9882 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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