---
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