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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_9
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: 0.9447027565592826
- name: Recall
type: recall
value: 0.9574217435207001
- name: F1
type: f1
value: 0.9510197258441992
- name: Accuracy
type: accuracy
value: 0.9884323893099322
---
<!-- 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_9
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: 0.0841
- Precision: 0.9447
- Recall: 0.9574
- F1: 0.9510
- Accuracy: 0.9884
## 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: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0779 | 1.0 | 1756 | 0.0640 | 0.9142 | 0.9359 | 0.9249 | 0.9836 |
| 0.0448 | 2.0 | 3512 | 0.0867 | 0.9220 | 0.9364 | 0.9291 | 0.9836 |
| 0.03 | 3.0 | 5268 | 0.0580 | 0.9263 | 0.9482 | 0.9371 | 0.9865 |
| 0.018 | 4.0 | 7024 | 0.0760 | 0.9330 | 0.9490 | 0.9409 | 0.9864 |
| 0.0108 | 5.0 | 8780 | 0.0733 | 0.9363 | 0.9544 | 0.9452 | 0.9873 |
| 0.0096 | 6.0 | 10536 | 0.0773 | 0.9413 | 0.9534 | 0.9473 | 0.9879 |
| 0.0039 | 7.0 | 12292 | 0.0755 | 0.9442 | 0.9561 | 0.9501 | 0.9885 |
| 0.0024 | 8.0 | 14048 | 0.0834 | 0.9425 | 0.9567 | 0.9496 | 0.9884 |
| 0.0006 | 9.0 | 15804 | 0.0841 | 0.9447 | 0.9574 | 0.9510 | 0.9884 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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