File size: 3,038 Bytes
473aad9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
---
license: apache-2.0
base_model: distilbert/distilbert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: distilbert-base-multilingual-cased-lora-text-classification
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-multilingual-cased-lora-text-classification
This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6074
- Precision: 0.7192
- Recall: 0.912
- F1 and accuracy: {'accuracy': 0.7146529562982005, 'f1': 0.8042328042328042}
## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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 | Precision | Recall | F1 and accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:----------------------------------------------------------:|
| No log | 1.0 | 388 | 0.6278 | 0.6723 | 0.96 | {'accuracy': 0.6735218508997429, 'f1': 0.7907742998352554} |
| 0.5998 | 2.0 | 776 | 0.6380 | 0.6713 | 0.956 | {'accuracy': 0.6709511568123393, 'f1': 0.7887788778877888} |
| 0.5865 | 3.0 | 1164 | 0.6196 | 0.6988 | 0.9 | {'accuracy': 0.6863753213367609, 'f1': 0.7867132867132868} |
| 0.5681 | 4.0 | 1552 | 0.6284 | 0.7018 | 0.932 | {'accuracy': 0.7017994858611826, 'f1': 0.8006872852233677} |
| 0.5681 | 5.0 | 1940 | 0.6072 | 0.7143 | 0.88 | {'accuracy': 0.6966580976863753, 'f1': 0.7885304659498208} |
| 0.5641 | 6.0 | 2328 | 0.6122 | 0.7031 | 0.9 | {'accuracy': 0.6915167095115681, 'f1': 0.7894736842105263} |
| 0.5356 | 7.0 | 2716 | 0.6074 | 0.7125 | 0.912 | {'accuracy': 0.7069408740359897, 'f1': 0.8} |
| 0.5407 | 8.0 | 3104 | 0.6016 | 0.7320 | 0.896 | {'accuracy': 0.7223650385604113, 'f1': 0.8057553956834531} |
| 0.5407 | 9.0 | 3492 | 0.6079 | 0.7192 | 0.912 | {'accuracy': 0.7146529562982005, 'f1': 0.8042328042328042} |
| 0.535 | 10.0 | 3880 | 0.6074 | 0.7192 | 0.912 | {'accuracy': 0.7146529562982005, 'f1': 0.8042328042328042} |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|