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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_5
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: 0.937014382542569
- name: Recall
type: recall
value: 0.9538875799394143
- name: F1
type: f1
value: 0.945375698440497
- name: Accuracy
type: accuracy
value: 0.9872971065631616
---
<!-- 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_5
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: 0.0716
- Precision: 0.9370
- Recall: 0.9539
- F1: 0.9454
- Accuracy: 0.9873
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0787 | 1.0 | 1756 | 0.0734 | 0.9024 | 0.9317 | 0.9168 | 0.9819 |
| 0.0389 | 2.0 | 3512 | 0.0706 | 0.9359 | 0.9440 | 0.9399 | 0.9854 |
| 0.023 | 3.0 | 5268 | 0.0632 | 0.9340 | 0.9483 | 0.9411 | 0.9864 |
| 0.0137 | 4.0 | 7024 | 0.0762 | 0.9368 | 0.9534 | 0.9450 | 0.9875 |
| 0.0054 | 5.0 | 8780 | 0.0716 | 0.9370 | 0.9539 | 0.9454 | 0.9873 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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