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

# nlp_til

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: 0.1994
- Precision: 0.4726
- Recall: 0.5278
- F1: 0.4987
- Accuracy: 0.9007

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 18

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 219  | 0.2462          | 0.3017    | 0.3623 | 0.3292 | 0.8584   |
| No log        | 2.0   | 438  | 0.2436          | 0.3176    | 0.3485 | 0.3323 | 0.8656   |
| 0.2463        | 3.0   | 657  | 0.2434          | 0.3333    | 0.4792 | 0.3932 | 0.8622   |
| 0.2463        | 4.0   | 876  | 0.2402          | 0.3398    | 0.3567 | 0.3480 | 0.8675   |
| 0.2453        | 5.0   | 1095 | 0.2388          | 0.3299    | 0.3708 | 0.3491 | 0.8686   |
| 0.2453        | 6.0   | 1314 | 0.2381          | 0.3230    | 0.3740 | 0.3467 | 0.8689   |
| 0.2421        | 7.0   | 1533 | 0.2384          | 0.3448    | 0.3508 | 0.3477 | 0.8691   |
| 0.2421        | 8.0   | 1752 | 0.2343          | 0.3427    | 0.3711 | 0.3563 | 0.8705   |
| 0.2421        | 9.0   | 1971 | 0.2334          | 0.3448    | 0.3433 | 0.3440 | 0.8713   |
| 0.2388        | 10.0  | 2190 | 0.2314          | 0.3696    | 0.4533 | 0.4072 | 0.8768   |
| 0.2388        | 11.0  | 2409 | 0.2238          | 0.3846    | 0.4643 | 0.4207 | 0.8812   |
| 0.2337        | 12.0  | 2628 | 0.2216          | 0.3968    | 0.4703 | 0.4305 | 0.8832   |
| 0.2337        | 13.0  | 2847 | 0.2135          | 0.4169    | 0.4939 | 0.4521 | 0.8898   |
| 0.2268        | 14.0  | 3066 | 0.2117          | 0.4387    | 0.5200 | 0.4759 | 0.8919   |
| 0.2268        | 15.0  | 3285 | 0.2059          | 0.4565    | 0.5146 | 0.4838 | 0.8963   |
| 0.2197        | 16.0  | 3504 | 0.2043          | 0.4669    | 0.5359 | 0.4990 | 0.8977   |
| 0.2197        | 17.0  | 3723 | 0.2005          | 0.4701    | 0.5356 | 0.5007 | 0.8997   |
| 0.2197        | 18.0  | 3942 | 0.1994          | 0.4726    | 0.5278 | 0.4987 | 0.9007   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1