README.md

metadata

license: apache-2.0
base_model: albert/albert-base-v2
tags:
  - trl
  - sft
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: classify-ISIN-STEP8_binary
    results: []

classify-ISIN-STEP8_binary

This model is a fine-tuned version of albert/albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0001
• Accuracy: 1.0
• F1: 1.0
• Precision: 1.0
• Recall: 1.0
• Accuracy Label Gd622:no: 1.0
• Accuracy Label Gd622:yes: 1.0

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
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 10
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Accuracy Label Gd622:no	Accuracy Label Gd622:yes
0.0992	0.1610	100	0.0070	0.9987	0.9987	0.9987	0.9987	0.9982	1.0
0.0082	0.3221	200	0.0002	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	0.4831	300	0.0001	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	0.6441	400	0.0001	1.0	1.0	1.0	1.0	1.0	1.0
0.0001	0.8052	500	0.0001	1.0	1.0	1.0	1.0	1.0	1.0
0.0052	0.9662	600	0.0001	1.0	1.0	1.0	1.0	1.0	1.0

Framework versions

• Transformers 4.43.3
• Pytorch 2.4.0
• Datasets 2.20.0
• Tokenizers 0.19.1