End of training

README.md

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+---
+license: mit
+base_model: microsoft/deberta-v3-large
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: deberta-v3-large-finetuned-ner
+  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. -->
+
+# deberta-v3-large-finetuned-ner
+
+This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0364
+- Precision: 0.9641
+- Recall: 0.9716
+- F1: 0.9678
+- Accuracy: 0.9931
+
+## 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: 16
+- eval_batch_size: 16
+- 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     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1237        | 1.0   | 878  | 0.0406          | 0.9492    | 0.9589 | 0.9540 | 0.9906   |
+| 0.0242        | 2.0   | 1756 | 0.0340          | 0.9550    | 0.9634 | 0.9592 | 0.9917   |
+| 0.0123        | 3.0   | 2634 | 0.0383          | 0.9630    | 0.9679 | 0.9654 | 0.9923   |
+| 0.0055        | 4.0   | 3512 | 0.0345          | 0.9633    | 0.9716 | 0.9674 | 0.9929   |
+| 0.0034        | 5.0   | 4390 | 0.0364          | 0.9641    | 0.9716 | 0.9678 | 0.9931   |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0
+- Datasets 2.17.0
+- Tokenizers 0.15.2

model.safetensors

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