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: 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
test_implementation
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: 0.6736
- 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