Training complete!

README.md

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+---
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- recall
+- precision
+- f1
+model-index:
+- name: xlm-roberta-base-finetuned-marc-en
+  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. -->
+
+# xlm-roberta-base-finetuned-marc-en
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8177
+- Accuracy: 0.8086
+- Recall: 0.5269
+- Precision: 0.6436
+- F1: 0.5794
+
+## 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: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 15
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.5098        | 1.0   | 309  | 0.4999          | 0.7498   | 0.0    | 0.0       | 0.0    |
+| 0.4698        | 2.0   | 618  | 0.4456          | 0.7959   | 0.3456 | 0.6816    | 0.4586 |
+| 0.3921        | 3.0   | 927  | 0.4620          | 0.8094   | 0.4561 | 0.6765    | 0.5448 |
+| 0.3771        | 4.0   | 1236 | 0.4446          | 0.8172   | 0.5156 | 0.6766    | 0.5852 |
+| 0.3454        | 5.0   | 1545 | 0.4567          | 0.8249   | 0.5609 | 0.6828    | 0.6159 |
+| 0.2713        | 6.0   | 1854 | 0.4726          | 0.8136   | 0.6176 | 0.6301    | 0.6237 |
+| 0.272         | 7.0   | 2163 | 0.5024          | 0.8108   | 0.6317 | 0.6194    | 0.6255 |
+| 0.2478        | 8.0   | 2472 | 0.5689          | 0.8051   | 0.6516 | 0.6021    | 0.6259 |
+| 0.1869        | 9.0   | 2781 | 0.6018          | 0.8044   | 0.7082 | 0.5910    | 0.6443 |
+| 0.1575        | 10.0  | 3090 | 0.6700          | 0.8108   | 0.4986 | 0.6617    | 0.5687 |
+| 0.1411        | 11.0  | 3399 | 0.7287          | 0.8157   | 0.5581 | 0.6545    | 0.6024 |
+| 0.1014        | 12.0  | 3708 | 0.8177          | 0.8086   | 0.5269 | 0.6436    | 0.5794 |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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