End of training

README.md

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+---
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+model-index:
+- name: xlm-roberta-base-Multilingual-Sentence-Segmentation-v4
+  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-Multilingual-Sentence-Segmentation-v4
+
+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: 0.0074
+- Precision: 0.9664
+- Recall: 0.9677
+- F1: 0.9670
+
+## 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: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
+| No log        | 0.2   | 100  | 0.0125          | 0.9320    | 0.9487 | 0.9403 |
+| No log        | 0.4   | 200  | 0.0099          | 0.9547    | 0.9513 | 0.9530 |
+| No log        | 0.6   | 300  | 0.0092          | 0.9616    | 0.9506 | 0.9561 |
+| No log        | 0.81  | 400  | 0.0083          | 0.9584    | 0.9618 | 0.9601 |
+| 0.0212        | 1.01  | 500  | 0.0082          | 0.9551    | 0.9642 | 0.9596 |
+| 0.0212        | 1.21  | 600  | 0.0084          | 0.9630    | 0.9614 | 0.9622 |
+| 0.0212        | 1.41  | 700  | 0.0079          | 0.9606    | 0.9648 | 0.9627 |
+| 0.0212        | 1.61  | 800  | 0.0077          | 0.9609    | 0.9661 | 0.9635 |
+| 0.0212        | 1.81  | 900  | 0.0076          | 0.9623    | 0.9649 | 0.9636 |
+| 0.0067        | 2.02  | 1000 | 0.0077          | 0.9598    | 0.9689 | 0.9643 |
+| 0.0067        | 2.22  | 1100 | 0.0075          | 0.9614    | 0.9680 | 0.9647 |
+| 0.0067        | 2.42  | 1200 | 0.0073          | 0.9626    | 0.9682 | 0.9654 |
+| 0.0067        | 2.62  | 1300 | 0.0075          | 0.9617    | 0.9692 | 0.9654 |
+| 0.0067        | 2.82  | 1400 | 0.0073          | 0.9658    | 0.9648 | 0.9653 |
+| 0.0054        | 3.02  | 1500 | 0.0076          | 0.9656    | 0.9663 | 0.9660 |
+| 0.0054        | 3.23  | 1600 | 0.0073          | 0.9625    | 0.9703 | 0.9664 |
+| 0.0054        | 3.43  | 1700 | 0.0073          | 0.9658    | 0.9659 | 0.9658 |
+| 0.0054        | 3.63  | 1800 | 0.0073          | 0.9626    | 0.9707 | 0.9666 |
+| 0.0054        | 3.83  | 1900 | 0.0073          | 0.9659    | 0.9677 | 0.9668 |
+| 0.0046        | 4.03  | 2000 | 0.0075          | 0.9671    | 0.9659 | 0.9665 |
+| 0.0046        | 4.23  | 2100 | 0.0075          | 0.9654    | 0.9687 | 0.9671 |
+| 0.0046        | 4.44  | 2200 | 0.0075          | 0.9662    | 0.9676 | 0.9669 |
+| 0.0046        | 4.64  | 2300 | 0.0074          | 0.9657    | 0.9684 | 0.9670 |
+| 0.0046        | 4.84  | 2400 | 0.0074          | 0.9664    | 0.9678 | 0.9671 |
+
+
+### Framework versions
+
+- Transformers 4.39.1
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2