---
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
---

<!-- 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. -->

# ADAPMIT-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 on the [Policy-Classification](https://huggingface.co/datasets/GIZ/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](https://github.com/mlco2/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