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

# roberta-base-finetuned-ner

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1185
- Precision: 0.7791
- Recall: 0.8034
- F1: 0.7910
- Accuracy: 0.7680

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.19  | 50   | 1.2727          | 0.6213    | 0.4935 | 0.5501 | 0.4934   |
| No log        | 0.37  | 100  | 1.2623          | 0.6398    | 0.5312 | 0.5805 | 0.5263   |
| No log        | 0.56  | 150  | 1.2519          | 0.6609    | 0.5693 | 0.6117 | 0.5593   |
| No log        | 0.75  | 200  | 1.2423          | 0.6713    | 0.5940 | 0.6303 | 0.5815   |
| No log        | 0.93  | 250  | 1.2330          | 0.6828    | 0.6167 | 0.6481 | 0.6014   |
| No log        | 1.12  | 300  | 1.2241          | 0.6914    | 0.6388 | 0.6640 | 0.6219   |
| No log        | 1.31  | 350  | 1.2158          | 0.6962    | 0.6540 | 0.6744 | 0.6350   |
| No log        | 1.49  | 400  | 1.2076          | 0.6995    | 0.6637 | 0.6811 | 0.6434   |
| No log        | 1.68  | 450  | 1.2000          | 0.7048    | 0.6767 | 0.6905 | 0.6545   |
| 1.2539        | 1.87  | 500  | 1.1926          | 0.7093    | 0.6880 | 0.6985 | 0.6645   |
| 1.2539        | 2.05  | 550  | 1.1859          | 0.7148    | 0.6990 | 0.7068 | 0.6736   |
| 1.2539        | 2.24  | 600  | 1.1793          | 0.7206    | 0.7092 | 0.7148 | 0.6824   |
| 1.2539        | 2.43  | 650  | 1.1733          | 0.7269    | 0.7209 | 0.7239 | 0.6935   |
| 1.2539        | 2.61  | 700  | 1.1676          | 0.7340    | 0.7306 | 0.7323 | 0.7025   |
| 1.2539        | 2.8   | 750  | 1.1620          | 0.7385    | 0.7380 | 0.7382 | 0.7091   |
| 1.2539        | 2.99  | 800  | 1.1569          | 0.7429    | 0.7451 | 0.744  | 0.7160   |
| 1.2539        | 3.17  | 850  | 1.1521          | 0.7496    | 0.7560 | 0.7528 | 0.7265   |
| 1.2539        | 3.36  | 900  | 1.1476          | 0.7539    | 0.7622 | 0.7580 | 0.7325   |
| 1.2539        | 3.54  | 950  | 1.1435          | 0.7552    | 0.7657 | 0.7604 | 0.7349   |
| 1.1751        | 3.73  | 1000 | 1.1399          | 0.7585    | 0.7718 | 0.7651 | 0.7405   |
| 1.1751        | 3.92  | 1050 | 1.1364          | 0.7626    | 0.7789 | 0.7706 | 0.7470   |
| 1.1751        | 4.1   | 1100 | 1.1332          | 0.7657    | 0.7835 | 0.7745 | 0.7513   |
| 1.1751        | 4.29  | 1150 | 1.1303          | 0.7700    | 0.7895 | 0.7796 | 0.7561   |
| 1.1751        | 4.48  | 1200 | 1.1278          | 0.7727    | 0.7934 | 0.7829 | 0.7589   |
| 1.1751        | 4.66  | 1250 | 1.1256          | 0.7732    | 0.7945 | 0.7837 | 0.7600   |
| 1.1751        | 4.85  | 1300 | 1.1237          | 0.7744    | 0.7960 | 0.7851 | 0.7614   |
| 1.1751        | 5.04  | 1350 | 1.1221          | 0.7748    | 0.7973 | 0.7859 | 0.7622   |
| 1.1751        | 5.22  | 1400 | 1.1208          | 0.7766    | 0.7995 | 0.7879 | 0.7643   |
| 1.1751        | 5.41  | 1450 | 1.1198          | 0.7783    | 0.8021 | 0.7900 | 0.7665   |
| 1.1363        | 5.6   | 1500 | 1.1191          | 0.7789    | 0.8032 | 0.7908 | 0.7675   |
| 1.1363        | 5.78  | 1550 | 1.1187          | 0.7791    | 0.8034 | 0.7910 | 0.7680   |
| 1.1363        | 5.97  | 1600 | 1.1185          | 0.7791    | 0.8034 | 0.7910 | 0.7680   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2