End of training

README.md

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+---
+license: mit
+base_model: nielsr/lilt-xlm-roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: LILT-id
+  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. -->
+
+# LILT-id
+
+This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4723
+- Precision: 0.9132
+- Recall: 0.8998
+- F1: 0.9064
+- Accuracy: 0.9467
+
+## 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: 2
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 30
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 2.4390  | 200  | 0.2933          | 0.875     | 0.8557 | 0.8653 | 0.9208   |
+| No log        | 4.8780  | 400  | 0.3650          | 0.8934    | 0.8606 | 0.8767 | 0.9257   |
+| 0.3014        | 7.3171  | 600  | 0.3822          | 0.9055    | 0.8900 | 0.8977 | 0.9386   |
+| 0.3014        | 9.7561  | 800  | 0.4052          | 0.8980    | 0.8826 | 0.8903 | 0.9386   |
+| 0.0365        | 12.1951 | 1000 | 0.4668          | 0.8966    | 0.8900 | 0.8933 | 0.9386   |
+| 0.0365        | 14.6341 | 1200 | 0.4664          | 0.9123    | 0.8900 | 0.9010 | 0.9435   |
+| 0.0365        | 17.0732 | 1400 | 0.4993          | 0.8978    | 0.8802 | 0.8889 | 0.9370   |
+| 0.0091        | 19.5122 | 1600 | 0.4723          | 0.9132    | 0.8998 | 0.9064 | 0.9467   |
+| 0.0091        | 21.9512 | 1800 | 0.4826          | 0.9089    | 0.9022 | 0.9055 | 0.9467   |
+| 0.0004        | 24.3902 | 2000 | 0.4790          | 0.9086    | 0.8998 | 0.9042 | 0.9467   |
+| 0.0004        | 26.8293 | 2200 | 0.4807          | 0.9086    | 0.8998 | 0.9042 | 0.9467   |
+| 0.0004        | 29.2683 | 2400 | 0.4818          | 0.9086    | 0.8998 | 0.9042 | 0.9467   |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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