---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- favsbot
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-NER-favsbot
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: favsbot
      type: favsbot
      config: default
      split: train
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.8571428571428571
    - name: Recall
      type: recall
      value: 0.96
    - name: F1
      type: f1
      value: 0.9056603773584904
    - name: Accuracy
      type: accuracy
      value: 0.9583333333333334
---

<!-- 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-base-cased-NER-favsbot

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the favsbot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0992
- Precision: 0.8571
- Recall: 0.96
- F1: 0.9057
- Accuracy: 0.9583

## 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: 1.5e-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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 10   | 1.7643          | 0.0       | 0.0    | 0.0    | 0.5694   |
| No log        | 2.0   | 20   | 1.1420          | 0.0       | 0.0    | 0.0    | 0.5833   |
| No log        | 3.0   | 30   | 0.7946          | 0.9375    | 0.6    | 0.7317 | 0.8056   |
| No log        | 4.0   | 40   | 0.5625          | 0.8182    | 0.72   | 0.7660 | 0.8611   |
| No log        | 5.0   | 50   | 0.4217          | 0.8148    | 0.88   | 0.8462 | 0.9306   |
| No log        | 6.0   | 60   | 0.3082          | 0.8519    | 0.92   | 0.8846 | 0.9444   |
| No log        | 7.0   | 70   | 0.2386          | 0.8148    | 0.88   | 0.8462 | 0.9444   |
| No log        | 8.0   | 80   | 0.1965          | 0.8148    | 0.88   | 0.8462 | 0.9444   |
| No log        | 9.0   | 90   | 0.1626          | 0.8148    | 0.88   | 0.8462 | 0.9444   |
| No log        | 10.0  | 100  | 0.1465          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
| No log        | 11.0  | 110  | 0.1314          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
| No log        | 12.0  | 120  | 0.1215          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
| No log        | 13.0  | 130  | 0.1160          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
| No log        | 14.0  | 140  | 0.1104          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
| No log        | 15.0  | 150  | 0.1050          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
| No log        | 16.0  | 160  | 0.1012          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
| No log        | 17.0  | 170  | 0.0997          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
| No log        | 18.0  | 180  | 0.0997          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
| No log        | 19.0  | 190  | 0.0995          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
| No log        | 20.0  | 200  | 0.0992          | 0.8571    | 0.96   | 0.9057 | 0.9583   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.12.1