transformers tensorboard safetensors bert text-classification generated_from_trainer dataset:arxiv_dataset base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-uncased
datasets:
- arxiv_dataset
license: apache-2.0
metrics:
- accuracy
- precision
- recall
- f1
tags:
- generated_from_trainer
model-index:
- name: baseline_BERT_10K_steps
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: arxiv_dataset
      type: arxiv_dataset
      config: default
      split: train
      args: default
    metrics:
    - type: accuracy
      value: 0.9905994544286106
      name: Accuracy
    - type: precision
      value: 0.7827298050139275
      name: Precision
    - type: recall
      value: 0.05172572480441785
      name: Recall
    - type: f1
      value: 0.09703876370543037
      name: F1
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# baseline_BERT_10K_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: 0.0356
- Accuracy: 0.9906
- Precision: 0.7827
- Recall: 0.0517
- F1: 0.0970
- Hamming: 0.0094

## 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: 10000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     | Hamming |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log        | 0.0   | 500   | 0.1602          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| No log        | 0.0   | 1000  | 0.0573          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| No log        | 0.0   | 1500  | 0.0504          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| No log        | 0.01  | 2000  | 0.0492          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| No log        | 0.01  | 2500  | 0.0488          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| No log        | 0.01  | 3000  | 0.0485          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| No log        | 0.01  | 3500  | 0.0477          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| No log        | 0.01  | 4000  | 0.0467          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| No log        | 0.01  | 4500  | 0.0455          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| No log        | 0.01  | 5000  | 0.0442          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| No log        | 0.02  | 5500  | 0.0422          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| No log        | 0.02  | 6000  | 0.0408          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| No log        | 0.02  | 6500  | 0.0394          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| No log        | 0.02  | 7000  | 0.0385          | 0.9902   | 1.0       | 0.0011 | 0.0022 | 0.0098  |
| No log        | 0.02  | 7500  | 0.0376          | 0.9903   | 0.7949    | 0.0057 | 0.0113 | 0.0097  |
| No log        | 0.02  | 8000  | 0.0368          | 0.9903   | 0.8071    | 0.0146 | 0.0287 | 0.0097  |
| No log        | 0.03  | 8500  | 0.0363          | 0.9905   | 0.7372    | 0.0465 | 0.0874 | 0.0095  |
| No log        | 0.03  | 9000  | 0.0359          | 0.9905   | 0.7811    | 0.0381 | 0.0727 | 0.0095  |
| No log        | 0.03  | 9500  | 0.0357          | 0.9906   | 0.8029    | 0.0562 | 0.1051 | 0.0094  |
| 0.0665        | 0.03  | 10000 | 0.0356          | 0.9906   | 0.7827    | 0.0517 | 0.0970 | 0.0094  |


### Framework versions

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