---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: jpmodel_remote-work_distilbert-base-uncased_0517
  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. -->

# jpmodel_remote-work_distilbert-base-uncased_0517

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: 0.4293
- Accuracy: {'accuracy': 0.9476614699331849}
- F1: {'f1': 0.9316670582946814}
- Precision: {'precision': 0.9211843955719234}
- Recall: {'recall': 0.9476614699331849}

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | F1                         | Precision                         | Recall                         |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:|:---------------------------------:|:------------------------------:|
| No log        | 1.0   | 449  | 0.2487          | {'accuracy': 0.9532293986636972} | {'f1': 0.9304040652863451} | {'precision': 0.9086462864767536} | {'recall': 0.9532293986636972} |
| 0.2176        | 2.0   | 898  | 0.2366          | {'accuracy': 0.9532293986636972} | {'f1': 0.9304040652863451} | {'precision': 0.9086462864767536} | {'recall': 0.9532293986636972} |
| 0.1796        | 3.0   | 1347 | 0.2228          | {'accuracy': 0.9526726057906458} | {'f1': 0.9320734514025724} | {'precision': 0.9182722571033837} | {'recall': 0.9526726057906458} |
| 0.1469        | 4.0   | 1796 | 0.2856          | {'accuracy': 0.9437639198218263} | {'f1': 0.9282364670603435} | {'precision': 0.9135405361560103} | {'recall': 0.9437639198218263} |
| 0.1045        | 5.0   | 2245 | 0.3386          | {'accuracy': 0.9437639198218263} | {'f1': 0.9280406899884679} | {'precision': 0.9132963430863958} | {'recall': 0.9437639198218263} |
| 0.0742        | 6.0   | 2694 | 0.3708          | {'accuracy': 0.9437639198218263} | {'f1': 0.928813770000516}  | {'precision': 0.9155656638103506} | {'recall': 0.9437639198218263} |
| 0.0401        | 7.0   | 3143 | 0.3897          | {'accuracy': 0.9437639198218263} | {'f1': 0.9291849652492169} | {'precision': 0.9199457677450203} | {'recall': 0.9437639198218263} |
| 0.0263        | 8.0   | 3592 | 0.4163          | {'accuracy': 0.9471046770601337} | {'f1': 0.9322848244083336} | {'precision': 0.9235426032908877} | {'recall': 0.9471046770601337} |
| 0.0149        | 9.0   | 4041 | 0.4249          | {'accuracy': 0.9471046770601337} | {'f1': 0.9313864813181381} | {'precision': 0.9211608097664751} | {'recall': 0.9471046770601337} |
| 0.0149        | 10.0  | 4490 | 0.4293          | {'accuracy': 0.9476614699331849} | {'f1': 0.9316670582946814} | {'precision': 0.9211843955719234} | {'recall': 0.9476614699331849} |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1