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---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: multilingual_dbert_linsearch_only_abstract
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. -->
# multilingual_dbert_linsearch_only_abstract
This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5452
- Accuracy: 0.6465
- F1 Macro: 0.5744
- Precision Macro: 0.5998
- Recall Macro: 0.5660
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|
| 1.3208 | 1.0 | 19722 | 1.2841 | 0.6142 | 0.5160 | 0.5368 | 0.5359 |
| 1.1135 | 2.0 | 39444 | 1.1921 | 0.6449 | 0.5597 | 0.5673 | 0.5575 |
| 0.8989 | 3.0 | 59166 | 1.2967 | 0.6495 | 0.5643 | 0.5834 | 0.5573 |
| 0.7155 | 4.0 | 78888 | 1.5452 | 0.6465 | 0.5744 | 0.5998 | 0.5660 |
| 0.5373 | 5.0 | 98610 | 1.7780 | 0.6400 | 0.5669 | 0.5895 | 0.5605 |
### Framework versions
- Transformers 4.50.1
- Pytorch 2.5.1+cu121
- Datasets 3.4.1
- Tokenizers 0.21.1
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