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---
base_model: ''
tags:
- generated_from_trainer
datasets:
- conll2003
model-index:
- name: span-marker-roberta-base-conll03
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. -->
# span-marker-roberta-base-conll03
This model is a fine-tuned version of [](https://huggingface.co/) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0121
- Overall Precision: 0.9357
- Overall Recall: 0.9346
- Overall F1: 0.9351
- Overall Accuracy: 0.9870
## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0351 | 0.28 | 500 | 0.0272 | 0.8928 | 0.8251 | 0.8576 | 0.9662 |
| 0.0209 | 0.55 | 1000 | 0.0168 | 0.9066 | 0.9167 | 0.9116 | 0.9820 |
| 0.0169 | 0.83 | 1500 | 0.0120 | 0.9380 | 0.9291 | 0.9336 | 0.9863 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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