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

# 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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9217
- 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

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: 7.4e-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      |


### Framework versions

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