---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-uncased-english-cefr-lexical-evaluation-ep-v4
  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. -->

# distilbert-base-uncased-english-cefr-lexical-evaluation-ep-v4

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: 1.9206
- Accuracy: 0.6093
- F1: 0.6095
- Precision: 0.6174
- Recall: 0.6093

## 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: 0.0001
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.2735        | 1.0   | 346  | 1.4207          | 0.4721   | 0.4451 | 0.4941    | 0.4721 |
| 1.0356        | 2.0   | 692  | 1.2296          | 0.5380   | 0.5399 | 0.5786    | 0.5380 |
| 0.3684        | 3.0   | 1038 | 1.6360          | 0.5590   | 0.5619 | 0.5712    | 0.5590 |
| 0.1539        | 4.0   | 1384 | 2.1402          | 0.5844   | 0.5846 | 0.5901    | 0.5844 |
| 0.2342        | 5.0   | 1730 | 2.4715          | 0.5807   | 0.5828 | 0.5915    | 0.5807 |
| 0.0022        | 6.0   | 2076 | 2.4647          | 0.5742   | 0.5763 | 0.5802    | 0.5742 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3