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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: 1.0950
- Accuracy: 0.7742
- F1: 0.7742
- Precision: 0.7742
- Recall: 0.7742
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 48 | 2.8245 | 0.2796 | 0.2796 | 0.2796 | 0.2796 |
| No log | 2.0 | 96 | 2.2338 | 0.4301 | 0.4301 | 0.4301 | 0.4301 |
| No log | 3.0 | 144 | 1.9060 | 0.5269 | 0.5269 | 0.5269 | 0.5269 |
| No log | 4.0 | 192 | 1.5349 | 0.6022 | 0.6022 | 0.6022 | 0.6022 |
| No log | 5.0 | 240 | 1.4208 | 0.6882 | 0.6882 | 0.6882 | 0.6882 |
| No log | 6.0 | 288 | 1.3330 | 0.7204 | 0.7204 | 0.7204 | 0.7204 |
| No log | 7.0 | 336 | 1.2037 | 0.7097 | 0.7097 | 0.7097 | 0.7097 |
| No log | 8.0 | 384 | 1.1414 | 0.7419 | 0.7419 | 0.7419 | 0.7419 |
| No log | 9.0 | 432 | 1.0950 | 0.7742 | 0.7742 | 0.7742 | 0.7742 |
| No log | 10.0 | 480 | 1.0883 | 0.7634 | 0.7634 | 0.7634 | 0.7634 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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