---
license: mit
base_model: BAAI/bge-base-en-v1.5
tags:
- generated_from_trainer
model-index:
- name: IKI-Category-multilabel_bge
  results: []
---

<!-- 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. -->

# IKI-Category-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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4541
- Precision-micro: 0.75
- Precision-samples: 0.7708
- Precision-weighted: 0.7517
- Recall-micro: 0.7880
- Recall-samples: 0.7858
- Recall-weighted: 0.7880
- F1-micro: 0.7685
- F1-samples: 0.7537
- F1-weighted: 0.7615

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 15

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:|
| 0.8999        | 0.99  | 94   | 0.8742          | 0.3889          | 0.0272            | 0.1308             | 0.0169       | 0.0188         | 0.0169          | 0.0323   | 0.0202     | 0.0280      |
| 0.7377        | 2.0   | 189  | 0.6770          | 0.4727          | 0.4996            | 0.5333             | 0.5639       | 0.5782         | 0.5639          | 0.5143   | 0.4883     | 0.4998      |
| 0.5582        | 2.99  | 283  | 0.5552          | 0.5111          | 0.5585            | 0.5685             | 0.7229       | 0.7357         | 0.7229          | 0.5988   | 0.5959     | 0.6175      |
| 0.3943        | 4.0   | 378  | 0.4713          | 0.5616          | 0.6397            | 0.5869             | 0.7904       | 0.8071         | 0.7904          | 0.6567   | 0.6761     | 0.6611      |
| 0.2883        | 4.99  | 472  | 0.4555          | 0.6384          | 0.6969            | 0.6444             | 0.7446       | 0.7641         | 0.7446          | 0.6874   | 0.6901     | 0.6854      |
| 0.2112        | 6.0   | 567  | 0.4459          | 0.6443          | 0.6968            | 0.6637             | 0.7855       | 0.7942         | 0.7855          | 0.7079   | 0.7123     | 0.7068      |
| 0.1608        | 6.99  | 661  | 0.4212          | 0.6508          | 0.7071            | 0.6586             | 0.7904       | 0.7931         | 0.7904          | 0.7138   | 0.7161     | 0.7116      |
| 0.1247        | 8.0   | 756  | 0.4177          | 0.6633          | 0.7145            | 0.6650             | 0.7976       | 0.8006         | 0.7976          | 0.7243   | 0.7193     | 0.7195      |
| 0.1031        | 8.99  | 850  | 0.4435          | 0.7277          | 0.7523            | 0.7306             | 0.7855       | 0.7875         | 0.7855          | 0.7555   | 0.7425     | 0.7487      |
| 0.0851        | 10.0  | 945  | 0.4522          | 0.7380          | 0.7623            | 0.7465             | 0.7807       | 0.7795         | 0.7807          | 0.7588   | 0.7432     | 0.7516      |
| 0.074         | 10.99 | 1039 | 0.4548          | 0.7359          | 0.7663            | 0.7368             | 0.7855       | 0.7910         | 0.7855          | 0.7599   | 0.7490     | 0.7521      |
| 0.0648        | 12.0  | 1134 | 0.4430          | 0.7425          | 0.7676            | 0.7437             | 0.7783       | 0.7781         | 0.7783          | 0.76     | 0.7461     | 0.7540      |
| 0.0605        | 12.99 | 1228 | 0.4478          | 0.7366          | 0.7651            | 0.7379             | 0.7952       | 0.7948         | 0.7952          | 0.7648   | 0.7545     | 0.7579      |
| 0.0566        | 14.0  | 1323 | 0.4574          | 0.7506          | 0.7708            | 0.7519             | 0.7904       | 0.7893         | 0.7904          | 0.7700   | 0.7546     | 0.7625      |
| 0.0546        | 14.92 | 1410 | 0.4541          | 0.75            | 0.7708            | 0.7517             | 0.7880       | 0.7858         | 0.7880          | 0.7685   | 0.7537     | 0.7615      |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1