---
license: mit
base_model: FacebookAI/roberta-large
tags:
- generated_from_trainer
datasets:
- few-nerd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: few-nerd
      type: few-nerd
      config: supervised
      split: validation
      args: supervised
    metrics:
    - name: Precision
      type: precision
      value: 0.7844853130000198
    - name: Recall
      type: recall
      value: 0.8147760612215589
    - name: F1
      type: f1
      value: 0.799343826738054
    - name: Accuracy
      type: accuracy
      value: 0.9428779215112315
---

<!-- 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. -->

# bert-finetuned-ner

This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the few-nerd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2164
- Precision: 0.7845
- Recall: 0.8148
- F1: 0.7993
- Accuracy: 0.9429

## 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: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1953        | 1.0   | 32942 | 0.1933          | 0.7670    | 0.7968 | 0.7816 | 0.9395   |
| 0.1573        | 2.0   | 65884 | 0.2051          | 0.7850    | 0.8034 | 0.7941 | 0.9416   |
| 0.1256        | 3.0   | 98826 | 0.2164          | 0.7845    | 0.8148 | 0.7993 | 0.9429   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0