---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: balanced-augmented-roberta-gest-pred-seqeval-partialmatch-2
  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. -->

# balanced-augmented-roberta-gest-pred-seqeval-partialmatch-2

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5211
- Precision: 0.9245
- Recall: 0.9214
- F1: 0.9159
- Accuracy: 0.9027

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 3.0873        | 1.0   | 52   | 2.5606          | 0.1508    | 0.1205 | 0.1095 | 0.3096   |
| 2.2599        | 2.0   | 104  | 1.8545          | 0.3409    | 0.3827 | 0.3343 | 0.5265   |
| 1.7149        | 3.0   | 156  | 1.4711          | 0.5470    | 0.5222 | 0.4715 | 0.6087   |
| 1.3056        | 4.0   | 208  | 1.0879          | 0.6500    | 0.6103 | 0.5886 | 0.6919   |
| 0.9978        | 5.0   | 260  | 1.0036          | 0.7039    | 0.6766 | 0.6497 | 0.7221   |
| 0.7532        | 6.0   | 312  | 0.7722          | 0.7356    | 0.7552 | 0.7286 | 0.7842   |
| 0.5945        | 7.0   | 364  | 0.6766          | 0.8316    | 0.7902 | 0.7790 | 0.8053   |
| 0.473         | 8.0   | 416  | 0.5994          | 0.8602    | 0.8248 | 0.8224 | 0.8406   |
| 0.3762        | 9.0   | 468  | 0.5572          | 0.8725    | 0.8743 | 0.8600 | 0.8593   |
| 0.2943        | 10.0  | 520  | 0.5767          | 0.8893    | 0.8714 | 0.8659 | 0.8593   |
| 0.251         | 11.0  | 572  | 0.5480          | 0.8892    | 0.8765 | 0.8667 | 0.8633   |
| 0.2074        | 12.0  | 624  | 0.5652          | 0.8960    | 0.8866 | 0.8757 | 0.8714   |
| 0.1714        | 13.0  | 676  | 0.5254          | 0.9172    | 0.9087 | 0.9019 | 0.8875   |
| 0.1523        | 14.0  | 728  | 0.5788          | 0.9217    | 0.8900 | 0.8918 | 0.8790   |
| 0.1309        | 15.0  | 780  | 0.5209          | 0.9205    | 0.9141 | 0.9080 | 0.8961   |
| 0.1187        | 16.0  | 832  | 0.5030          | 0.9163    | 0.9138 | 0.9073 | 0.8961   |
| 0.1065        | 17.0  | 884  | 0.5449          | 0.9278    | 0.9212 | 0.9153 | 0.8986   |
| 0.0923        | 18.0  | 936  | 0.4965          | 0.9214    | 0.9180 | 0.9135 | 0.9012   |
| 0.0894        | 19.0  | 988  | 0.5171          | 0.9236    | 0.9189 | 0.9148 | 0.9007   |
| 0.0869        | 20.0  | 1040 | 0.5211          | 0.9245    | 0.9214 | 0.9159 | 0.9027   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2