ppsingh/mpnet-multilabel-sector-classifier

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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
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 200
• —num_epochs: 8
• —weight_decay: 0.001
• —gradientacumulationsteps: 1

Training results

Environmental Impact

Carbon emissions were estimated using the [codecarbon](https://github.com/mlco2/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