---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: attribute_minig_mslacerda
  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. -->

# attribute_minig_mslacerda

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5688
- Precision: 0.7424
- Recall: 0.7766
- F1: 0.7591
- Accuracy: 0.8673

## 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: 4.546321141328063e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 207
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 85   | 4.0840          | 0.0271    | 0.0229 | 0.0248 | 0.1523   |
| No log        | 2.0   | 170  | 2.1874          | 0.3096    | 0.2348 | 0.2671 | 0.5099   |
| No log        | 3.0   | 255  | 1.2897          | 0.4893    | 0.4684 | 0.4786 | 0.6940   |
| No log        | 4.0   | 340  | 0.8647          | 0.6485    | 0.6413 | 0.6449 | 0.8027   |
| No log        | 5.0   | 425  | 0.6897          | 0.6977    | 0.7051 | 0.7014 | 0.8414   |
| 1.93          | 6.0   | 510  | 0.6046          | 0.7177    | 0.7354 | 0.7264 | 0.8550   |
| 1.93          | 7.0   | 595  | 0.5833          | 0.7339    | 0.7602 | 0.7468 | 0.8628   |
| 1.93          | 8.0   | 680  | 0.5722          | 0.7474    | 0.7627 | 0.7550 | 0.8643   |
| 1.93          | 9.0   | 765  | 0.5704          | 0.7451    | 0.7695 | 0.7571 | 0.8694   |
| 1.93          | 10.0  | 850  | 0.5802          | 0.7485    | 0.7763 | 0.7622 | 0.8715   |
| 1.93          | 11.0  | 935  | 0.5723          | 0.7539    | 0.7838 | 0.7685 | 0.8754   |
| 0.1632        | 12.0  | 1020 | 0.5736          | 0.7497    | 0.7813 | 0.7652 | 0.8739   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1