mpnet-multilabel-sector-classifier

This model is a fine-tuned version of sentence-transformers/all-mpnet-base-v2 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2273
• Precision Micro: 0.8075
• Precision Weighted: 0.8110
• Precision Samples: 0.8365
• Recall Micro: 0.8897
• Recall Weighted: 0.8897
• Recall Samples: 0.8922
• F1-score: 0.8464

Model description

This model is trained for performing Multi Label Sector Classification.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 6.9e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 200
• num_epochs: 8
• weight_decay: 0.001
• gradient_acumulation_steps: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Precision Micro	Precision Weighted	Precision Samples	Recall Micro	Recall Weighted	Recall Samples	F1-score
0.4478	1.0	897	0.2277	0.6731	0.7183	0.7460	0.8822	0.8822	0.8989	0.7871
0.2241	2.0	1794	0.1862	0.7088	0.7485	0.7754	0.8933	0.8933	0.9110	0.8108
0.1647	3.0	2691	0.2025	0.6785	0.7023	0.7634	0.9124	0.9124	0.9252	0.8077
0.1232	4.0	3588	0.1839	0.7274	0.7322	0.7976	0.9029	0.9029	0.9134	0.8286
0.0899	5.0	4485	0.1889	0.7919	0.8007	0.8350	0.8909	0.8909	0.9060	0.8483
0.0653	6.0	5382	0.2039	0.7478	0.7544	0.8098	0.8973	0.8973	0.9114	0.8346
0.0462	7.0	6279	0.2149	0.7447	0.7500	0.8060	0.8989	0.8989	0.9107	0.8323
0.0336	8.0	7176	0.2181	0.7733	0.7780	0.8221	0.8909	0.8909	0.9031	0.8400

Environmental Impact

Carbon emissions were estimated using the codecarbon. The carbon emission reported are incluidng the hyperparamter search performed on subset of training data.

• Hardware Type: 16GB T4
• Hours used: 3
• Cloud Provider: Google Colab
• Carbon Emitted : 0.276132

Framework versions

• Transformers 4.28.0
• Pytorch 2.0.1+cu118
• Datasets 2.12.0
• Tokenizers 0.13.3