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

# distil-bert-imeoocap

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: 1.5520
- F1: 0.6416
- Precision: 0.6437
- Recall: 0.6442
- Accuracy: 0.6442

## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 0.4777        | 1.0   | 74   | 1.1554          | 0.6498 | 0.6489    | 0.6538 | 0.6538   |
| 0.395         | 2.0   | 148  | 1.2060          | 0.6062 | 0.6109    | 0.6135 | 0.6135   |
| 0.364         | 3.0   | 222  | 1.2625          | 0.6329 | 0.6436    | 0.6423 | 0.6423   |
| 0.3402        | 4.0   | 296  | 1.3512          | 0.6247 | 0.6330    | 0.6269 | 0.6269   |
| 0.3135        | 5.0   | 370  | 1.3587          | 0.6472 | 0.6442    | 0.6519 | 0.6519   |
| 0.307         | 6.0   | 444  | 1.4376          | 0.6258 | 0.6334    | 0.6288 | 0.6288   |
| 0.2903        | 7.0   | 518  | 1.3565          | 0.6502 | 0.6550    | 0.65   | 0.65     |
| 0.2931        | 8.0   | 592  | 1.4059          | 0.6310 | 0.6273    | 0.6365 | 0.6365   |
| 0.2805        | 9.0   | 666  | 1.3972          | 0.6357 | 0.6370    | 0.6365 | 0.6365   |
| 0.2772        | 10.0  | 740  | 1.4938          | 0.6205 | 0.6204    | 0.6308 | 0.6308   |
| 0.2767        | 11.0  | 814  | 1.4324          | 0.6256 | 0.6324    | 0.6269 | 0.6269   |
| 0.2634        | 12.0  | 888  | 1.5399          | 0.6457 | 0.6487    | 0.65   | 0.65     |
| 0.2829        | 13.0  | 962  | 1.4857          | 0.6369 | 0.6363    | 0.6385 | 0.6385   |
| 0.2444        | 14.0  | 1036 | 1.4879          | 0.6314 | 0.6385    | 0.6308 | 0.6308   |
| 0.2424        | 15.0  | 1110 | 1.5049          | 0.6357 | 0.6399    | 0.6365 | 0.6365   |
| 0.2332        | 16.0  | 1184 | 1.5277          | 0.6233 | 0.6268    | 0.625  | 0.625    |
| 0.2215        | 17.0  | 1258 | 1.5550          | 0.6390 | 0.6379    | 0.6423 | 0.6423   |
| 0.2235        | 18.0  | 1332 | 1.5593          | 0.6434 | 0.6449    | 0.6481 | 0.6481   |
| 0.213         | 19.0  | 1406 | 1.5631          | 0.6337 | 0.6303    | 0.6385 | 0.6385   |
| 0.2098        | 20.0  | 1480 | 1.5520          | 0.6416 | 0.6437    | 0.6442 | 0.6442   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2