---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- arxiv_dataset
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: baseline_BERT_50K_steps
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: arxiv_dataset
      type: arxiv_dataset
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9936787420400056
    - name: Precision
      type: precision
      value: 0.7967781908302355
    - name: Recall
      type: recall
      value: 0.4734468476760239
    - name: F1
      type: f1
      value: 0.5939610876970152
---

<!-- 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. -->

# baseline_BERT_50K_steps

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the arxiv_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0192
- Accuracy: 0.9937
- Precision: 0.7968
- Recall: 0.4734
- F1: 0.5940
- Hamming: 0.0063

## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 50000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     | Hamming |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.0343        | 0.03  | 10000 | 0.0315          | 0.9912   | 0.7679    | 0.1370 | 0.2326 | 0.0088  |
| 0.0244        | 0.06  | 20000 | 0.0234          | 0.9925   | 0.7813    | 0.3262 | 0.4602 | 0.0075  |
| 0.0219        | 0.09  | 30000 | 0.0210          | 0.9931   | 0.7572    | 0.4320 | 0.5502 | 0.0069  |
| 0.0204        | 0.12  | 40000 | 0.0197          | 0.9935   | 0.7738    | 0.4711 | 0.5857 | 0.0065  |
| 0.0197        | 0.15  | 50000 | 0.0192          | 0.9937   | 0.7968    | 0.4734 | 0.5940 | 0.0063  |


### Framework versions

- Transformers 4.37.2
- Pytorch 1.12.1+cu113
- Datasets 2.16.1
- Tokenizers 0.15.1