---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- fin
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: fin
      type: fin
      config: fin
      split: validation
      args: fin
    metrics:
    - name: Precision
      type: precision
      value: 0.9408740359897172
    - name: Recall
      type: recall
      value: 0.9682539682539683
    - name: F1
      type: f1
      value: 0.954367666232073
    - name: Accuracy
      type: accuracy
      value: 0.9930041974815111
---

<!-- 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 the fin dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0331
- Precision: 0.9409
- Recall: 0.9683
- F1: 0.9544
- Accuracy: 0.9930

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 64   | 0.0650          | 0.9457    | 0.9206 | 0.9330 | 0.9884   |
| No log        | 2.0   | 128  | 0.0366          | 0.9141    | 0.9577 | 0.9354 | 0.9924   |
| No log        | 3.0   | 192  | 0.0331          | 0.9409    | 0.9683 | 0.9544 | 0.9930   |


### Framework versions

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