pytorch bert token-classification dataset:cynthiachan/FeedRef_10pct transformers generated_from_trainer license:apache-2.0 model-index autotrain_compatible
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cynthiachan/FeedRef_10pct
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: training
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: cynthiachan/FeedRef_10pct
      type: cynthiachan/FeedRef_10pct
      args: FeedRef_10pct
    metrics:
    - name: Precision
      type: precision
      value: 0.8222222222222222
    - name: Recall
      type: recall
      value: 0.7662721893491125
    - name: F1
      type: f1
      value: 0.7932618683001531
    - name: Accuracy
      type: accuracy
      value: 0.9940363242071022
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# training

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the cynthiachan/FeedRef_10pct dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0447
- Precision: 0.8222
- Recall: 0.7663
- F1: 0.7933
- Accuracy: 0.9940

## 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: 5e-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: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1445        | 0.37  | 500  | 0.1098          | 0.2707    | 0.4556 | 0.3396 | 0.9747   |
| 0.0817        | 0.75  | 1000 | 0.0691          | 0.5357    | 0.4882 | 0.5108 | 0.9877   |
| 0.0729        | 1.12  | 1500 | 0.0780          | 0.7853    | 0.4112 | 0.5398 | 0.9887   |
| 0.0512        | 1.5   | 2000 | 0.0538          | 0.7311    | 0.6598 | 0.6936 | 0.9915   |
| 0.0564        | 1.87  | 2500 | 0.0624          | 0.7581    | 0.5562 | 0.6416 | 0.9909   |
| 0.0459        | 2.25  | 3000 | 0.0522          | 0.7841    | 0.6982 | 0.7387 | 0.9929   |
| 0.0408        | 2.62  | 3500 | 0.0482          | 0.8098    | 0.7308 | 0.7683 | 0.9935   |
| 0.0378        | 3.0   | 4000 | 0.0447          | 0.8222    | 0.7663 | 0.7933 | 0.9940   |


### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
