|
--- |
|
license: apache-2.0 |
|
base_model: distilbert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
model-index: |
|
- name: distilbert-base-uncased-finetuned-CEFR |
|
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-finetuned-CEFR |
|
|
|
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2528 |
|
- Accuracy: 0.3350 |
|
- Precision: 0.3202 |
|
- Recall: 0.6791 |
|
- F1: 0.2925 |
|
|
|
## 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: 15 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
| No log | 1.0 | 50 | 0.2342 | 0.3324 | 0.3240 | 0.6540 | 0.2960 | |
|
| No log | 2.0 | 100 | 0.2326 | 0.3330 | 0.3166 | 0.6658 | 0.2841 | |
|
| No log | 3.0 | 150 | 0.2362 | 0.3332 | 0.3171 | 0.6680 | 0.2882 | |
|
| No log | 4.0 | 200 | 0.2410 | 0.3335 | 0.3238 | 0.6722 | 0.2979 | |
|
| No log | 5.0 | 250 | 0.2468 | 0.3337 | 0.3254 | 0.6657 | 0.2964 | |
|
| No log | 6.0 | 300 | 0.2455 | 0.3341 | 0.3190 | 0.6697 | 0.2937 | |
|
| No log | 7.0 | 350 | 0.2404 | 0.3347 | 0.3226 | 0.6795 | 0.2931 | |
|
| No log | 8.0 | 400 | 0.2491 | 0.3341 | 0.3298 | 0.6732 | 0.2998 | |
|
| No log | 9.0 | 450 | 0.2489 | 0.3345 | 0.3213 | 0.6763 | 0.2949 | |
|
| 0.0385 | 10.0 | 500 | 0.2487 | 0.3349 | 0.3173 | 0.6780 | 0.2876 | |
|
| 0.0385 | 11.0 | 550 | 0.2570 | 0.3346 | 0.3264 | 0.6754 | 0.2971 | |
|
| 0.0385 | 12.0 | 600 | 0.2548 | 0.3348 | 0.3234 | 0.6746 | 0.2946 | |
|
| 0.0385 | 13.0 | 650 | 0.2533 | 0.3349 | 0.3219 | 0.6806 | 0.2942 | |
|
| 0.0385 | 14.0 | 700 | 0.2523 | 0.3350 | 0.3198 | 0.6801 | 0.2919 | |
|
| 0.0385 | 15.0 | 750 | 0.2528 | 0.3350 | 0.3202 | 0.6791 | 0.2925 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.40.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
|
|
