---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: trainer11
  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. -->

# trainer11

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0559
- Precision: 0.6119
- Recall: 0.5833
- F1: 0.5850
- Accuracy: 0.5833

## 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: 5e-05
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0243        | 0.57  | 30   | 2.3196          | 0.6424    | 0.5952 | 0.5823 | 0.5952   |
| 0.0329        | 1.13  | 60   | 2.9356          | 0.4952    | 0.5595 | 0.5177 | 0.5595   |
| 0.0724        | 1.7   | 90   | 3.0099          | 0.6234    | 0.5595 | 0.5412 | 0.5595   |
| 0.052         | 2.26  | 120  | 2.4391          | 0.6305    | 0.6190 | 0.6103 | 0.6190   |
| 0.0019        | 2.83  | 150  | 3.2342          | 0.6364    | 0.6071 | 0.6002 | 0.6071   |
| 0.0002        | 3.4   | 180  | 3.2336          | 0.6024    | 0.5714 | 0.5666 | 0.5714   |
| 0.0002        | 3.96  | 210  | 3.0605          | 0.6136    | 0.5833 | 0.5851 | 0.5833   |
| 0.0001        | 4.53  | 240  | 3.0569          | 0.6119    | 0.5833 | 0.5850 | 0.5833   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2