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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: baseline_BERT_10K_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: 0.9905994544286106
- name: Precision
type: precision
value: 0.7827298050139275
- name: Recall
type: recall
value: 0.05172572480441785
- name: F1
type: f1
value: 0.09703876370543037
---
<!-- 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_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
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