Model save

README.md

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+---
+library_name: transformers
+base_model: syssec-utd/py38-pylingual-v1.1.1-mlm
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: py38-pylingual-v1.1.1-segmenter
+  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. -->
+
+# py38-pylingual-v1.1.1-segmenter
+
+This model is a fine-tuned version of [syssec-utd/py38-pylingual-v1.1.1-mlm](https://huggingface.co/syssec-utd/py38-pylingual-v1.1.1-mlm) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0614
+- Precision: 0.8780
+- Recall: 0.8852
+- F1: 0.8816
+- Accuracy: 0.9694
+
+## 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: 48
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 3
+- total_train_batch_size: 144
+- total_eval_batch_size: 24
+- 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: 2
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2647        | 1.0   | 1490 | 0.1043          | 0.7752    | 0.8197 | 0.7968 | 0.9480   |
+| 0.1113        | 2.0   | 2980 | 0.0614          | 0.8780    | 0.8852 | 0.8816 | 0.9694   |
+
+
+### Framework versions
+
+- Transformers 4.46.1
+- Pytorch 2.4.1+cu121
+- Datasets 3.0.1
+- Tokenizers 0.20.3

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