|
|
--- |
|
|
library_name: transformers |
|
|
license: mit |
|
|
base_model: FacebookAI/xlm-roberta-large |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
metrics: |
|
|
- accuracy |
|
|
- f1 |
|
|
- precision |
|
|
- recall |
|
|
model-index: |
|
|
- name: xlmr_synset_classifier_marked |
|
|
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. --> |
|
|
|
|
|
# xlmr_synset_classifier_marked |
|
|
|
|
|
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on an unknown dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.5065 |
|
|
- Accuracy: 0.8542 |
|
|
- F1: 0.8462 |
|
|
- Precision: 0.8503 |
|
|
- Recall: 0.8542 |
|
|
- F1 Macro: 0.6998 |
|
|
- Precision Macro: 0.6940 |
|
|
- Recall Macro: 0.7209 |
|
|
- F1 Micro: 0.8542 |
|
|
- Precision Micro: 0.8542 |
|
|
- Recall Micro: 0.8542 |
|
|
|
|
|
## 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: 32 |
|
|
- eval_batch_size: 32 |
|
|
- seed: 42 |
|
|
- gradient_accumulation_steps: 4 |
|
|
- total_train_batch_size: 128 |
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
|
- lr_scheduler_type: linear |
|
|
- lr_scheduler_warmup_steps: 50 |
|
|
- num_epochs: 5 |
|
|
- mixed_precision_training: Native AMP |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | F1 Macro | Precision Macro | Recall Macro | F1 Micro | Precision Micro | Recall Micro | |
|
|
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:| |
|
|
| 3.4502 | 0.6221 | 100 | 1.8070 | 0.6245 | 0.5355 | 0.5022 | 0.6245 | 0.2357 | 0.2456 | 0.2565 | 0.6245 | 0.6245 | 0.6245 | |
|
|
| 1.1977 | 1.2442 | 200 | 0.7296 | 0.8116 | 0.7934 | 0.8034 | 0.8116 | 0.5365 | 0.5496 | 0.5643 | 0.8116 | 0.8116 | 0.8116 | |
|
|
| 0.724 | 1.8663 | 300 | 0.6379 | 0.8282 | 0.8150 | 0.8301 | 0.8282 | 0.5981 | 0.5903 | 0.6309 | 0.8282 | 0.8282 | 0.8282 | |
|
|
| 0.5655 | 2.4883 | 400 | 0.5609 | 0.8398 | 0.8267 | 0.8326 | 0.8398 | 0.6235 | 0.6094 | 0.6519 | 0.8398 | 0.8398 | 0.8398 | |
|
|
| 0.5095 | 3.1104 | 500 | 0.5166 | 0.8488 | 0.8389 | 0.8470 | 0.8488 | 0.6594 | 0.6492 | 0.6862 | 0.8488 | 0.8488 | 0.8488 | |
|
|
| 0.4206 | 3.7325 | 600 | 0.4964 | 0.8479 | 0.8396 | 0.8412 | 0.8479 | 0.6778 | 0.6770 | 0.6923 | 0.8479 | 0.8479 | 0.8479 | |
|
|
| 0.386 | 4.3546 | 700 | 0.5091 | 0.8502 | 0.8418 | 0.8468 | 0.8502 | 0.6949 | 0.6930 | 0.7146 | 0.8502 | 0.8502 | 0.8502 | |
|
|
| 0.3463 | 4.9767 | 800 | 0.5065 | 0.8542 | 0.8462 | 0.8503 | 0.8542 | 0.6998 | 0.6940 | 0.7209 | 0.8542 | 0.8542 | 0.8542 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.45.2 |
|
|
- Pytorch 2.3.1+cu121 |
|
|
- Datasets 2.20.0 |
|
|
- Tokenizers 0.20.3 |
|
|
|
