metadata
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- pytorch
- amazon-rating
- DistilBERTForSequenceClassification
- generated_from_trainer
metrics:
- accuracy
- matthews_correlation
model-index:
- name: distilbert-base-amazon-multi
results: []
datasets:
- mteb/amazon_reviews_multi
language:
- en
- de
- es
- fr
- ja
- zh
library_name: transformers
pipeline_tag: text-classification
distilbert-base-amazon-multi
This model is a fine-tuned version of distilbert-base-multilingual-cased on the mteb/amazon_reviews_multi dataset. It achieves the following results on the evaluation set:
- Loss: 0.9292
- Accuracy: 0.6055
- Matthews Correlation: 0.5072
Training procedure
This model was fine tuned on Google Colab using a single NVIDIA V100 GPU with 16GB of VRAM. It took around 13 hours to finish the finetuning of 10_000 steps.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 320
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 100000
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Matthews Correlation |
|---|---|---|---|---|---|
| 1.0008 | 0.26 | 10000 | 1.0027 | 0.5616 | 0.4520 |
| 0.9545 | 0.51 | 20000 | 0.9705 | 0.5810 | 0.4788 |
| 0.9216 | 0.77 | 30000 | 0.9415 | 0.5883 | 0.4868 |
| 0.8765 | 1.03 | 40000 | 0.9495 | 0.5891 | 0.4871 |
| 0.8837 | 1.28 | 50000 | 0.9254 | 0.5992 | 0.4997 |
| 0.8753 | 1.54 | 60000 | 0.9199 | 0.6014 | 0.5029 |
| 0.8572 | 1.8 | 70000 | 0.9108 | 0.6090 | 0.5117 |
| 0.7851 | 2.05 | 80000 | 0.9276 | 0.6052 | 0.5066 |
| 0.7918 | 2.31 | 90000 | 0.9292 | 0.6055 | 0.5072 |
| 0.793 | 2.57 | 100000 | 0.9288 | 0.6064 | 0.5084 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0