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---
license: apache-2.0
base_model: BAAI/bge-base-en-v1.5
tags:
- generated_from_trainer
- transformers
model-index:
- name: TAPP-multilabel-bge
results: []
datasets:
- GIZ/policy_classification
co2_eq_emissions:
emissions: 71.4552917731392
source: codecarbon
training_type: fine-tuning
on_cloud: true
cpu_model: Intel(R) Xeon(R) CPU @ 2.30GHz
ram_total_size: 12.6747894287109
hours_used: 1.36
hardware_used: 1 x Tesla T4
pipeline_tag: text-classification
library_name: transformers
---
<!-- 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. -->
# TAPP-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.
*The loss function BCEWithLogitsLoss is modified with pos_weight to focus on recall, therefore instead of loss the evaluation metrics are used to assess the model performance during training*
It achieves the following results on the evaluation set:
- Precision-micro: 0.7772
- Precision-samples: 0.7644
- Precision-weighted: 0.7756
- Recall-micro: 0.8329
- Recall-samples: 0.7920
- Recall-weighted: 0.8329
- F1-micro: 0.8041
- F1-samples: 0.7609
- F1-weighted: 0.8029
## Model description
The purpose of this model is to predict multiple labels simultaneously from a given input data. Specifically, the model will predict four labels -
ActionLabel, PlansLabel, PolicyLabel, and TargetLabel - that are relevant to a particular task or application
- **Target**: Targets are an intention to achieve a specific result, for example, to reduce GHG emissions to a specific level
(a GHG target) or increase energy efficiency or renewable energy to a specific level (a non-GHG target), typically by
a certain date.
- **Action**: Actions are an intention to implement specific means of achieving GHG reductions, usually in forms of concrete projects.
- **Policies**: Policies are domestic planning documents such as policies, regulations or guidlines.
- **Plans**:Plans are broader than specific policies or actions, such as a general intention to ‘improve efficiency’, ‘develop renewable energy’, etc.
*The terms come from the World Bank's NDC platform and WRI's publication*
## Intended uses & limitations
More information needed
## Training and evaluation data
- Training Dataset: 10031
| Class | Positive Count of Class|
|:-------------|:--------|
| Action | 5416 |
| Plans | 2140 |
| Policy | 1396|
| Target | 2911 |
- Validation Dataset: 932
| Class | Positive Count of Class|
|:-------------|:--------|
| Action | 513 |
| Plans | 198 |
| Policy | 122 |
| Target | 256 |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.4e-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: 7
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:|
| 0.7161 | 1.0 | 627 | 0.6322 | 0.5931 | 0.6373 | 0.6274 | 0.8219 | 0.7833 | 0.8219 | 0.6890 | 0.6728 | 0.7000 |
| 0.4549 | 2.0 | 1254 | 0.5420 | 0.6639 | 0.6891 | 0.7049 | 0.8090 | 0.7684 | 0.8090 | 0.7293 | 0.7048 | 0.7409 |
| 0.2599 | 3.0 | 1881 | 0.6966 | 0.7354 | 0.7396 | 0.7346 | 0.8219 | 0.7845 | 0.8219 | 0.7762 | 0.7425 | 0.7713 |
| 0.1405 | 4.0 | 2508 | 0.7530 | 0.7569 | 0.7494 | 0.7569 | 0.8292 | 0.7899 | 0.8292 | 0.7914 | 0.7505 | 0.7905 |
| 0.0681 | 5.0 | 3135 | 0.8234 | 0.7596 | 0.7535 | 0.7599 | 0.8356 | 0.7945 | 0.8356 | 0.7958 | 0.7546 | 0.7953 |
| 0.0291 | 6.0 | 3762 | 0.8849 | 0.7773 | 0.7640 | 0.7776 | 0.8301 | 0.7890 | 0.8301 | 0.8028 | 0.7597 | 0.8027 |
| 0.0147 | 7.0 | 4389 | 0.9217 | 0.7772 | 0.7644 | 0.7756 | 0.8329 | 0.7920 | 0.8329 | 0.8041 | 0.7609 | 0.8029 |
|label | precision |recall |f1-score| support|
|:-------------:|:---------:|:-----:|:------:|:------:|
|Action |0.826 |0.883 |0.853 | 513.0 |
|Plans |0.653 |0.646 |0.649 | 198.0 |
|Policy |0.726 |0.803 |0.762 | 122.0 |
|Target |0.791 |0.890 |0.838 | 256.0 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.07145 kg of CO2
- **Hours Used**: 1.36 hours
### Training Hardware
- **On Cloud**: yes
- **GPU Model**: 1 x Tesla T4
- **CPU Model**: Intel(R) Xeon(R) CPU @ 2.30GHz
- **RAM Size**: 12.67 GB
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |