---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: distilbert-base-uncased
metrics:
- precision
- recall
- accuracy
model-index:
- name: multilabel_classification
  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. -->

# multilabel_classification

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2810
- F1 Micro: 0.8770
- F1 Macro: 0.7787
- F1 Weighted: 0.8672
- Precision: 0.8702
- Recall: 0.8770
- Accuracy: 0.8770

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | F1 Weighted | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------:|:------:|:--------:|
| No log        | 1.0   | 406  | 0.2865          | 0.8643   | 0.7287   | 0.8438      | 0.8620    | 0.8643 | 0.8643   |
| 0.2729        | 2.0   | 812  | 0.2924          | 0.8737   | 0.7671   | 0.8616      | 0.8671    | 0.8737 | 0.8737   |
| 0.216         | 3.0   | 1218 | 0.2810          | 0.8770   | 0.7787   | 0.8672      | 0.8702    | 0.8770 | 0.8770   |
| 0.1868        | 4.0   | 1624 | 0.2813          | 0.8787   | 0.7802   | 0.8685      | 0.8725    | 0.8787 | 0.8787   |
| 0.1728        | 5.0   | 2030 | 0.2944          | 0.8748   | 0.7794   | 0.8664      | 0.8673    | 0.8748 | 0.8748   |
| 0.1728        | 6.0   | 2436 | 0.2937          | 0.8825   | 0.7967   | 0.8760      | 0.8762    | 0.8825 | 0.8825   |
| 0.155         | 7.0   | 2842 | 0.3007          | 0.8848   | 0.8039   | 0.8795      | 0.8789    | 0.8848 | 0.8848   |
| 0.151         | 8.0   | 3248 | 0.3007          | 0.8875   | 0.8070   | 0.8818      | 0.8819    | 0.8875 | 0.8875   |
| 0.1359        | 9.0   | 3654 | 0.3031          | 0.8870   | 0.8077   | 0.8818      | 0.8814    | 0.8870 | 0.8870   |
| 0.1359        | 10.0  | 4060 | 0.3035          | 0.8881   | 0.8086   | 0.8826      | 0.8826    | 0.8881 | 0.8881   |


### Framework versions

- PEFT 0.11.1
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.19.1
- Tokenizers 0.15.1