GIZ
/

Edit model card

ADAPMIT-multilabel-bge

This model is a fine-tuned version of BAAI/bge-base-en-v1.5 on the on the Policy-Classification dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3101
  • Precision-micro: 0.9058
  • Precision-samples: 0.8647
  • Precision-weighted: 0.9058
  • Recall-micro: 0.9305
  • Recall-samples: 0.8693
  • Recall-weighted: 0.9305
  • F1-micro: 0.9180
  • F1-samples: 0.8622
  • F1-weighted: 0.9180

Model description

The purpose of this model is to predict multiple labels simultaneously from a given input data. Specifically, the model will predict 2 labels - AdaptationLabel, MitigationLabel - that are relevant to a particular task or application

Intended uses & limitations

More information needed

Training and evaluation data

  • Training Dataset: 12538

    Class Positive Count of Class
    AdaptationLabel 5439
    MitigationLabel 6659
  • Validation Dataset: 1190

    Class Positive Count of Class
    AdaptationLabel 533
    MitigationLabel 604

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 4.08e-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: cosine
  • lr_scheduler_warmup_steps: 300
  • num_epochs: 4

Training results

Training Loss Epoch Step Validation Loss Precision-micro Precision-samples Precision-weighted Recall-micro Recall-samples Recall-weighted F1-micro F1-samples F1-weighted
0.3368 1.0 784 0.2917 0.8651 0.8450 0.8664 0.9138 0.8542 0.9138 0.8888 0.8437 0.8890
0.1807 2.0 1568 0.2549 0.9092 0.8643 0.9094 0.9156 0.8571 0.9156 0.9124 0.8571 0.9123
0.0955 3.0 2352 0.2988 0.9069 0.8660 0.9072 0.9252 0.8655 0.9252 0.9160 0.8613 0.9160
0.0495 4.0 3136 0.3101 0.9058 0.8647 0.9058 0.9305 0.8693 0.9305 0.9180 0.8622 0.9180
label precision recall f1-score support
:-------------: :---------: :-----: :------: :------:
AdaptationLabel 0.910 0.928 0.919 533.0
MitigationLabel 0.902 0.932 0.917 604.0

Environmental Impact

Carbon emissions were measured using CodeCarbon.

  • Carbon Emitted: 0.04051 kg of CO2
  • Hours Used: 0.994 hours

Training Hardware

  • On Cloud: yes
  • GPU Model: 1 x Tesla T4
  • CPU Model: Intel(R) Xeon(R) CPU @ 2.00GHz
  • RAM Size: 12.67 GB

Framework versions

  • Transformers 4.38.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2
Downloads last month
27
Safetensors
Model size
109M params
Tensor type
F32
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for GIZ/ADAPMIT-multilabel-bge_f

Finetuned
(256)
this model

Dataset used to train GIZ/ADAPMIT-multilabel-bge_f

Collection including GIZ/ADAPMIT-multilabel-bge_f