transformers tensorboard safetensors xlm-roberta generated_from_trainer base_model:xlm-roberta-base license:mit endpoints_compatible region:us
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: tmp
  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. -->

# tmp

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3272
- Precision: 0.5560
- Recall: 0.3209
- F1: 0.4069

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.7083        | 0.35  | 500  | 0.4423          | 0.2785    | 0.0529 | 0.0889 |
| 0.4849        | 0.7   | 1000 | 0.4009          | 0.3623    | 0.1803 | 0.2408 |
| 0.4021        | 1.04  | 1500 | 0.3621          | 0.5027    | 0.2212 | 0.3072 |
| 0.3276        | 1.39  | 2000 | 0.3606          | 0.4006    | 0.3077 | 0.3481 |
| 0.2857        | 1.74  | 2500 | 0.3432          | 0.5073    | 0.25   | 0.3349 |
| 0.251         | 2.09  | 3000 | 0.3481          | 0.4431    | 0.3413 | 0.3856 |
| 0.2184        | 2.43  | 3500 | 0.3309          | 0.5274    | 0.3353 | 0.4100 |
| 0.2162        | 2.78  | 4000 | 0.3411          | 0.4167    | 0.3726 | 0.3934 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
