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

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: 1.8186
- F1: 0.6341
- Precision: 0.6365
- Recall: 0.6365
- Accuracy: 0.6365

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 0.1961        | 1.0   | 74   | 1.6080          | 0.6314 | 0.6285    | 0.6385 | 0.6385   |
| 0.1845        | 2.0   | 148  | 1.7125          | 0.6298 | 0.6317    | 0.6385 | 0.6385   |
| 0.1717        | 3.0   | 222  | 1.9402          | 0.6226 | 0.6364    | 0.6385 | 0.6385   |
| 0.176         | 4.0   | 296  | 1.8028          | 0.6169 | 0.6253    | 0.6192 | 0.6192   |
| 0.1679        | 5.0   | 370  | 1.6948          | 0.6243 | 0.6285    | 0.625  | 0.625    |
| 0.168         | 6.0   | 444  | 1.8304          | 0.6317 | 0.6336    | 0.6385 | 0.6385   |
| 0.1617        | 7.0   | 518  | 1.7457          | 0.6286 | 0.6310    | 0.6308 | 0.6308   |
| 0.1677        | 8.0   | 592  | 1.8071          | 0.6422 | 0.6382    | 0.65   | 0.65     |
| 0.171         | 9.0   | 666  | 1.8177          | 0.6323 | 0.6326    | 0.6385 | 0.6385   |
| 0.1683        | 10.0  | 740  | 1.8265          | 0.6347 | 0.6370    | 0.6365 | 0.6365   |
| 0.1808        | 11.0  | 814  | 1.7734          | 0.6304 | 0.6365    | 0.6308 | 0.6308   |
| 0.1757        | 12.0  | 888  | 1.7727          | 0.6244 | 0.6296    | 0.6231 | 0.6231   |
| 0.1897        | 13.0  | 962  | 1.8449          | 0.6374 | 0.6377    | 0.6404 | 0.6404   |
| 0.1674        | 14.0  | 1036 | 1.8244          | 0.6455 | 0.6462    | 0.6481 | 0.6481   |
| 0.1746        | 15.0  | 1110 | 1.8186          | 0.6341 | 0.6365    | 0.6365 | 0.6365   |


### Framework versions

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