Model save

README.md

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+---
+license: apache-2.0
+base_model: google-bert/bert-base-multilingual-uncased
+tags:
+- generated_from_trainer
+metrics:
+- f1
+- accuracy
+model-index:
+- name: bert-base-multilingual-uncased-MLTC-rob
+  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. -->
+
+# bert-base-multilingual-uncased-MLTC-rob
+
+This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3796
+- F1: 0.8594
+- F1 Weighted: 0.8595
+- Roc Auc: 0.8540
+- Accuracy: 0.5784
+- Hamming Loss: 0.1459
+- Jaccard Score: 0.7535
+- Zero One Loss: 0.4216
+
+## 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 | F1     | F1 Weighted | Roc Auc | Accuracy | Hamming Loss | Jaccard Score | Zero One Loss |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:-------:|:--------:|:------------:|:-------------:|:-------------:|
+| 0.4034        | 1.0   | 146  | 0.3955          | 0.8282 | 0.8293      | 0.8291  | 0.5270   | 0.1710       | 0.7067        | 0.4730        |
+| 0.4045        | 2.0   | 292  | 0.3662          | 0.8387 | 0.8397      | 0.8426  | 0.5578   | 0.1575       | 0.7222        | 0.4422        |
+| 0.2843        | 3.0   | 438  | 0.3796          | 0.8594 | 0.8595      | 0.8540  | 0.5784   | 0.1459       | 0.7535        | 0.4216        |
+| 0.209         | 4.0   | 584  | 0.3766          | 0.8446 | 0.8383      | 0.8425  | 0.5450   | 0.1575       | 0.7311        | 0.4550        |
+| 0.2469        | 5.0   | 730  | 0.3608          | 0.8568 | 0.8548      | 0.8541  | 0.5784   | 0.1459       | 0.7494        | 0.4216        |
+| 0.2266        | 6.0   | 876  | 0.3702          | 0.8462 | 0.8428      | 0.8451  | 0.5630   | 0.1549       | 0.7334        | 0.4370        |
+| 0.1273        | 7.0   | 1022 | 0.3739          | 0.8561 | 0.8539      | 0.8547  | 0.5835   | 0.1452       | 0.7483        | 0.4165        |
+| 0.1376        | 8.0   | 1168 | 0.3910          | 0.8568 | 0.8549      | 0.8567  | 0.5861   | 0.1433       | 0.7494        | 0.4139        |
+| 0.1073        | 9.0   | 1314 | 0.3973          | 0.8566 | 0.8550      | 0.8567  | 0.5784   | 0.1433       | 0.7492        | 0.4216        |
+| 0.0752        | 10.0  | 1460 | 0.4025          | 0.8542 | 0.8525      | 0.8541  | 0.5707   | 0.1459       | 0.7455        | 0.4293        |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.1.2
+- Datasets 2.19.1
+- Tokenizers 0.19.1

model.safetensors

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