---
license: mit
base_model: BAAI/bge-base-en-v1.5
tags:
- generated_from_trainer
model-index:
- name: SECTOR-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. -->

# SECTOR-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: 0.6114
- Precision-micro: 0.6428
- Precision-samples: 0.7488
- Precision-weighted: 0.6519
- Recall-micro: 0.7855
- Recall-samples: 0.8627
- Recall-weighted: 0.7855
- F1-micro: 0.7071
- F1-samples: 0.7638
- F1-weighted: 0.7109

## 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: 7.04e-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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:|
| 0.7077        | 1.0   | 633  | 0.5490          | 0.4226          | 0.5465            | 0.4954             | 0.8211       | 0.8908         | 0.8211          | 0.5580   | 0.6243     | 0.5977      |
| 0.4546        | 2.0   | 1266 | 0.5009          | 0.4899          | 0.6127            | 0.5202             | 0.8438       | 0.9023         | 0.8438          | 0.6199   | 0.6822     | 0.6366      |
| 0.3105        | 3.0   | 1899 | 0.4947          | 0.5005          | 0.6593            | 0.5317             | 0.8508       | 0.8970         | 0.8508          | 0.6303   | 0.7125     | 0.6474      |
| 0.2044        | 4.0   | 2532 | 0.5430          | 0.5757          | 0.7044            | 0.5970             | 0.8106       | 0.8801         | 0.8106          | 0.6733   | 0.7379     | 0.6834      |
| 0.1314        | 5.0   | 3165 | 0.5633          | 0.6132          | 0.7385            | 0.6271             | 0.8065       | 0.8772         | 0.8065          | 0.6967   | 0.7606     | 0.7032      |
| 0.0892        | 6.0   | 3798 | 0.6073          | 0.6425          | 0.7499            | 0.6545             | 0.7844       | 0.8610         | 0.7844          | 0.7064   | 0.7634     | 0.7113      |
| 0.0721        | 7.0   | 4431 | 0.6114          | 0.6428          | 0.7488            | 0.6519             | 0.7855       | 0.8627         | 0.7855          | 0.7071   | 0.7638     | 0.7109      |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2