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test_implementation

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test_implementation
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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- arxiv_dataset
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: test_implementation
  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: 0.5925759148656968
    - name: Precision
      type: precision
      value: 0.00904383876000648
    - name: Recall
      type: recall
      value: 0.37505752416014726
    - name: F1
      type: f1
      value: 0.017661795045162184
---

<!-- 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. -->

# test_implementation

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: 0.6736
- Accuracy: 0.5926
- Precision: 0.0090
- Recall: 0.3751
- F1: 0.0177
- Hamming: 0.4074

## 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: 8
- eval_batch_size: 8
- 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Hamming |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.7077        | 0.0   | 5    | 0.6857          | 0.5529   | 0.0089    | 0.4040 | 0.0173 | 0.4471  |
| 0.6801        | 0.0   | 10   | 0.6736          | 0.5926   | 0.0090    | 0.3751 | 0.0177 | 0.4074  |


### Framework versions

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