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---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-finetuned-WebClassification-v2-smalllinguaMultiv2
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. -->
# roberta-finetuned-WebClassification-v2-smalllinguaMultiv2
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: 0.8644
- Accuracy: 0.8387
- F1: 0.8387
- Precision: 0.8387
- Recall: 0.8387
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 95 | 2.3654 | 0.4409 | 0.4409 | 0.4409 | 0.4409 |
| No log | 2.0 | 190 | 1.8455 | 0.5269 | 0.5269 | 0.5269 | 0.5269 |
| No log | 3.0 | 285 | 1.4468 | 0.6344 | 0.6344 | 0.6344 | 0.6344 |
| No log | 4.0 | 380 | 1.1099 | 0.7419 | 0.7419 | 0.7419 | 0.7419 |
| No log | 5.0 | 475 | 1.0515 | 0.7634 | 0.7634 | 0.7634 | 0.7634 |
| 1.6355 | 6.0 | 570 | 0.9938 | 0.7312 | 0.7312 | 0.7312 | 0.7312 |
| 1.6355 | 7.0 | 665 | 0.8275 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
| 1.6355 | 8.0 | 760 | 0.8344 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
| 1.6355 | 9.0 | 855 | 0.8516 | 0.8065 | 0.8065 | 0.8065 | 0.8065 |
| 1.6355 | 10.0 | 950 | 0.8723 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
| 0.2827 | 11.0 | 1045 | 0.8644 | 0.8387 | 0.8387 | 0.8387 | 0.8387 |
| 0.2827 | 12.0 | 1140 | 0.9343 | 0.8065 | 0.8065 | 0.8065 | 0.8065 |
| 0.2827 | 13.0 | 1235 | 1.0181 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
| 0.2827 | 14.0 | 1330 | 1.0068 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
| 0.2827 | 15.0 | 1425 | 1.0085 | 0.8065 | 0.8065 | 0.8065 | 0.8065 |
| 0.0485 | 16.0 | 1520 | 1.0257 | 0.8280 | 0.8280 | 0.8280 | 0.8280 |
| 0.0485 | 17.0 | 1615 | 1.0305 | 0.8172 | 0.8172 | 0.8172 | 0.8172 |
| 0.0485 | 18.0 | 1710 | 1.0648 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
| 0.0485 | 19.0 | 1805 | 1.0677 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
| 0.0485 | 20.0 | 1900 | 1.0687 | 0.7957 | 0.7957 | 0.7957 | 0.7957 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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