Model save

README.md

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+---
+license: mit
+base_model: microsoft/Phi-3-mini-4k-instruct
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: phi-3-mini-NER-PII-Vast3
+  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. -->
+
+# phi-3-mini-NER-PII-Vast3
+
+This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1122
+- Precision: 0.6826
+- Recall: 0.8382
+- F1: 0.7524
+- Accuracy: 0.9697
+
+## 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: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step  | Accuracy | F1     | Validation Loss | Precision | Recall |
+|:-------------:|:-----:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
+| 0.1017        | 1.0   | 11105 | 0.9693   | 0.7506 | 0.1123          | 0.6807    | 0.8364 |
+| 0.0782        | 2.0   | 22210 | 0.1119   | 0.6819 | 0.8382          | 0.7520    | 0.9697 |
+| 0.0944        | 3.0   | 33315 | 0.1122   | 0.6826 | 0.8382          | 0.7524    | 0.9697 |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.3.0
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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