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---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: finerweb-binary-classifier-xlmr-gemma3
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. -->
# finerweb-binary-classifier-xlmr-gemma3
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1484
- Precision: 0.7851
- Recall: 0.7983
- F1 Macro: 0.7913
- Accuracy: 0.8438
## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 0
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| No log | 0 | 0 | 0.2350 | 0.3796 | 0.5 | 0.4316 | 0.7593 |
| 0.1059 | 0.7812 | 1000 | 0.1000 | 0.8077 | 0.7659 | 0.7830 | 0.8517 |
| 0.0825 | 1.5625 | 2000 | 0.0995 | 0.8129 | 0.7662 | 0.7850 | 0.8540 |
| 0.0563 | 2.3438 | 3000 | 0.1118 | 0.7910 | 0.8195 | 0.8031 | 0.8488 |
| 0.0315 | 3.125 | 4000 | 0.1183 | 0.8049 | 0.7861 | 0.7947 | 0.8545 |
| 0.0359 | 3.9062 | 5000 | 0.1234 | 0.8174 | 0.7528 | 0.7766 | 0.8524 |
| 0.0213 | 4.6875 | 6000 | 0.1431 | 0.7791 | 0.8259 | 0.7960 | 0.8374 |
| 0.0162 | 5.4688 | 7000 | 0.1312 | 0.7904 | 0.8026 | 0.7961 | 0.8478 |
| 0.0111 | 6.25 | 8000 | 0.1509 | 0.7817 | 0.8155 | 0.7955 | 0.8411 |
| 0.013 | 7.0312 | 9000 | 0.1242 | 0.7998 | 0.7994 | 0.7996 | 0.8536 |
| 0.0135 | 7.8125 | 10000 | 0.1292 | 0.8162 | 0.7570 | 0.7794 | 0.8530 |
| 0.0094 | 8.5938 | 11000 | 0.1463 | 0.7814 | 0.8172 | 0.7957 | 0.8406 |
| 0.0063 | 9.375 | 12000 | 0.1427 | 0.7799 | 0.8202 | 0.7954 | 0.8390 |
| 0.0082 | 10.1562 | 13000 | 0.1369 | 0.7838 | 0.8082 | 0.7944 | 0.8430 |
| 0.0081 | 10.9375 | 14000 | 0.1390 | 0.7880 | 0.7840 | 0.7859 | 0.8446 |
| 0.0058 | 11.7188 | 15000 | 0.1421 | 0.7833 | 0.8072 | 0.7937 | 0.8426 |
| 0.0061 | 12.5 | 16000 | 0.1580 | 0.7646 | 0.8190 | 0.7819 | 0.8225 |
| 0.005 | 13.2812 | 17000 | 0.1414 | 0.7924 | 0.7963 | 0.7943 | 0.8486 |
| 0.0031 | 14.0625 | 18000 | 0.1403 | 0.7935 | 0.8052 | 0.7990 | 0.8501 |
| 0.0032 | 14.8438 | 19000 | 0.1412 | 0.7958 | 0.7873 | 0.7914 | 0.8496 |
| 0.0046 | 15.625 | 20000 | 0.1458 | 0.7852 | 0.8119 | 0.7967 | 0.8441 |
| 0.0027 | 16.4062 | 21000 | 0.1490 | 0.7832 | 0.8115 | 0.7952 | 0.8425 |
| 0.0025 | 17.1875 | 22000 | 0.1442 | 0.7886 | 0.7995 | 0.7938 | 0.8463 |
| 0.0025 | 17.9688 | 23000 | 0.1431 | 0.7962 | 0.7811 | 0.7881 | 0.8490 |
| 0.0017 | 18.75 | 24000 | 0.1496 | 0.7834 | 0.8036 | 0.7925 | 0.8427 |
| 0.0022 | 19.5312 | 25000 | 0.1484 | 0.7851 | 0.7983 | 0.7913 | 0.8438 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.1
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