metadata
license: mit
base_model: BAAI/bge-base-en-v1.5
tags:
- generated_from_trainer
model-index:
- name: ADAPMIT-multilabel-bge
results: []
datasets:
- GIZ/policy_classification
library_name: transformers
co2_eq_emissions:
emissions: 40.5174303026829
source: codecarbon
training_type: fine-tuning
on_cloud: true
cpu_model: Intel(R) Xeon(R) CPU @ 2.00GHz
ram_total_size: 12.6747894287109
hours_used: 0.994
hardware_used: 1 x Tesla T4
pipeline_tag: text-classification
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