---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: WITHINAPPS_NDD-claroline_test-tags
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# WITHINAPPS_NDD-claroline_test-tags

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: 0.0464
- Accuracy: 0.9876
- F1: 0.9878
- Precision: 0.9885
- Recall: 0.9876

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 0.9978 | 111  | 0.0467          | 0.9876   | 0.9878 | 0.9885    | 0.9876 |
| No log        | 1.9955 | 222  | 0.0464          | 0.9876   | 0.9878 | 0.9885    | 0.9876 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1