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---
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
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CONDITIONAL-multilabel-bge
This model is a fine-tuned version of [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [Policy-Classification](https://huggingface.co/datasets/GIZ/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](https://github.com/mlco2/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 |