license: mit
base_model: BAAI/bge-base-en-v1.5
tags:
- generated_from_trainer
model-index:
- name: CONDITIONAL-multilabel-bge
results: []
datasets:
- GIZ/policy_classification
library_name: transformers
pipeline_tag: text-classification
co2_eq_emissions:
emissions: 28.4522411264774
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: 0.702
hardware_used: 1 x Tesla T4
CONDITIONAL-multilabel-bge
This model is a fine-tuned version of BAAI/bge-base-en-v1.5 on the Policy-Classification dataset. It achieves the following results on the evaluation set:
- Loss: 0.5295
- Precision-micro: 0.5138
- Precision-samples: 0.1866
- Precision-weighted: 0.5169
- Recall-micro: 0.7378
- Recall-samples: 0.1874
- Recall-weighted: 0.7378
- F1-micro: 0.6058
- F1-samples: 0.1852
- F1-weighted: 0.6065
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 - ConditionalLabel, UnconditionalLabel - that are relevant to a particular task or application
- Conditional: In context of climate policy documents if certain Target/Action/Plan/Policy commitment is being made conditionally.
- Unconditional: In context of climate policy documents if certain Target/Action/Plan/Policy commitment is being made unconditionally.
Intended uses & limitations
The dataset sometimes does not include the sub-heading/heading which indicates that the paragraph belongs to Conditional/Unconditional category. But has been copied from the relevant document from those sub-headings. This makes the assessment of Conditonality very difficult. Annotator when given only the paragraph without the full long context had a difficulty in assessing the conditionality of commitments being made in paragraph.
Training and evaluation data
Training Dataset: 5901
Class Positive Count of Class ConditionalLabel 1986 UnconditionalLabel 1312 Validation Dataset: 1190
Class Positive Count of Class ConditionalLabel 192 UnconditionalLabel 136
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.02e-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: 200
- num_epochs: 6
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.5361 | 1.0 | 369 | 0.4405 | 0.3405 | 0.1655 | 0.4102 | 0.6311 | 0.1622 | 0.6311 | 0.4423 | 0.1622 | 0.4503 |
| 0.3692 | 2.0 | 738 | 0.3437 | 0.4631 | 0.1794 | 0.4929 | 0.6890 | 0.1761 | 0.6890 | 0.5539 | 0.1762 | 0.5604 |
| 0.182 | 3.0 | 1107 | 0.3915 | 0.4702 | 0.1857 | 0.4871 | 0.7470 | 0.1891 | 0.7470 | 0.5771 | 0.1854 | 0.5800 |
| 0.0757 | 4.0 | 1476 | 0.4713 | 0.4960 | 0.1882 | 0.4986 | 0.7530 | 0.1908 | 0.7530 | 0.5981 | 0.1877 | 0.5987 |
| 0.0298 | 5.0 | 1845 | 0.4971 | 0.5161 | 0.1840 | 0.5184 | 0.7317 | 0.1857 | 0.7317 | 0.6053 | 0.1829 | 0.6058 |
| 0.0152 | 6.0 | 2214 | 0.5295 | 0.5138 | 0.1866 | 0.5169 | 0.7378 | 0.1874 | 0.7378 | 0.6058 | 0.1852 | 0.6065 |
| label | precision | recall | f1-score | support |
|---|---|---|---|---|
| ConditionalLabel | 0.490 | 0.760 | 0.595 | 192.0 |
| UnconditionalLabel | 0.555 | 0.706 | 0.621 | 136.0 |
Environmental Impact
Carbon emissions were measured using CodeCarbon.
- Carbon Emitted: 0.02845 kg of CO2
- Hours Used: 0.702 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