metadata
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
baseline_BERT_50K_steps
This model is a fine-tuned version of 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