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---
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
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