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---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- fursov/gec_ner_val3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner-gec-roberta-v3
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: fursov/gec_ner_val3
type: fursov/gec_ner_val3
metrics:
- name: Precision
type: precision
value: 0.5705440070765149
- name: Recall
type: recall
value: 0.43481191856545776
- name: F1
type: f1
value: 0.493515436703776
- name: Accuracy
type: accuracy
value: 0.9566099116988466
---
<!-- 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. -->
# ner-gec-roberta-v3
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the fursov/gec_ner_val3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1759
- Precision: 0.5705
- Recall: 0.4348
- F1: 0.4935
- Accuracy: 0.9566
## 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: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.2421 | 1.15 | 500 | 0.9349 | 0.0868 | 0.2389 | 0.1631 | 0.0591 |
| 0.2065 | 2.3 | 1000 | 0.9381 | 0.2139 | 0.2182 | 0.3006 | 0.1660 |
| 0.1729 | 3.46 | 1500 | 0.9446 | 0.3066 | 0.1986 | 0.4014 | 0.2480 |
| 0.1558 | 4.61 | 2000 | 0.9485 | 0.3556 | 0.1899 | 0.4544 | 0.2921 |
| 0.1546 | 5.76 | 2500 | 0.1857 | 0.4823 | 0.3191 | 0.3841 | 0.9504 |
| 0.1343 | 6.91 | 3000 | 0.1784 | 0.5302 | 0.3794 | 0.4423 | 0.9535 |
| 0.1163 | 8.06 | 3500 | 0.1767 | 0.5563 | 0.4094 | 0.4717 | 0.9556 |
| 0.1045 | 9.22 | 4000 | 0.1783 | 0.5595 | 0.4328 | 0.4880 | 0.9554 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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