---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner-fine-tune-roberta-new
  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. -->

# ner-fine-tune-roberta-new

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: 0.3320
- Precision: 0.2696
- Recall: 0.3767
- F1: 0.3143
- Accuracy: 0.9389

## 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: 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 122  | 0.2264          | 0.0       | 0.0    | 0.0    | 0.9542   |
| No log        | 2.0   | 244  | 0.1874          | 0.2348    | 0.1349 | 0.1713 | 0.9571   |
| No log        | 3.0   | 366  | 0.1739          | 0.2420    | 0.2279 | 0.2347 | 0.9528   |
| No log        | 4.0   | 488  | 0.1656          | 0.1939    | 0.2233 | 0.2076 | 0.9472   |
| 0.2548        | 5.0   | 610  | 0.1740          | 0.3243    | 0.2767 | 0.2986 | 0.9573   |
| 0.2548        | 6.0   | 732  | 0.2087          | 0.2562    | 0.2628 | 0.2595 | 0.9464   |
| 0.2548        | 7.0   | 854  | 0.1921          | 0.2773    | 0.2953 | 0.2860 | 0.9495   |
| 0.2548        | 8.0   | 976  | 0.2038          | 0.2602    | 0.3860 | 0.3109 | 0.9397   |
| 0.0748        | 9.0   | 1098 | 0.2324          | 0.2371    | 0.3093 | 0.2684 | 0.9398   |
| 0.0748        | 10.0  | 1220 | 0.2329          | 0.2852    | 0.3442 | 0.3119 | 0.9436   |
| 0.0748        | 11.0  | 1342 | 0.2670          | 0.2521    | 0.3535 | 0.2943 | 0.9356   |
| 0.0748        | 12.0  | 1464 | 0.2607          | 0.2509    | 0.3186 | 0.2807 | 0.9395   |
| 0.033         | 13.0  | 1586 | 0.2645          | 0.2655    | 0.3791 | 0.3123 | 0.9359   |
| 0.033         | 14.0  | 1708 | 0.2947          | 0.2838    | 0.4442 | 0.3463 | 0.9398   |
| 0.033         | 15.0  | 1830 | 0.2807          | 0.2945    | 0.3349 | 0.3134 | 0.9451   |
| 0.033         | 16.0  | 1952 | 0.2990          | 0.2910    | 0.3302 | 0.3094 | 0.9448   |
| 0.0181        | 17.0  | 2074 | 0.2915          | 0.2799    | 0.3651 | 0.3169 | 0.9425   |
| 0.0181        | 18.0  | 2196 | 0.2853          | 0.2868    | 0.3535 | 0.3167 | 0.9424   |
| 0.0181        | 19.0  | 2318 | 0.2991          | 0.2918    | 0.3814 | 0.3306 | 0.9440   |
| 0.0181        | 20.0  | 2440 | 0.2863          | 0.2762    | 0.3744 | 0.3179 | 0.9408   |
| 0.0111        | 21.0  | 2562 | 0.3280          | 0.2796    | 0.3628 | 0.3158 | 0.9409   |
| 0.0111        | 22.0  | 2684 | 0.3135          | 0.2772    | 0.3372 | 0.3043 | 0.9431   |
| 0.0111        | 23.0  | 2806 | 0.3282          | 0.2632    | 0.3698 | 0.3075 | 0.9404   |
| 0.0111        | 24.0  | 2928 | 0.3306          | 0.2597    | 0.3884 | 0.3113 | 0.9369   |
| 0.0078        | 25.0  | 3050 | 0.3135          | 0.2743    | 0.3605 | 0.3116 | 0.9414   |
| 0.0078        | 26.0  | 3172 | 0.3320          | 0.2646    | 0.3791 | 0.3117 | 0.9374   |
| 0.0078        | 27.0  | 3294 | 0.3273          | 0.2659    | 0.3791 | 0.3126 | 0.9381   |
| 0.0078        | 28.0  | 3416 | 0.3290          | 0.2616    | 0.3674 | 0.3056 | 0.9380   |
| 0.0055        | 29.0  | 3538 | 0.3366          | 0.2656    | 0.3860 | 0.3147 | 0.9372   |
| 0.0055        | 30.0  | 3660 | 0.3320          | 0.2696    | 0.3767 | 0.3143 | 0.9389   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1