End of training

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: cybersecurity-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. -->
+
+# cybersecurity-ner
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2398
+- Precision: 0.7853
+- Recall: 0.7984
+- F1: 0.7918
+- Accuracy: 0.9504
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 167  | 0.2454          | 0.8038    | 0.7664 | 0.7846 | 0.9489   |
+| No log        | 2.0   | 334  | 0.2225          | 0.7697    | 0.8230 | 0.7954 | 0.9512   |
+| 0.0449        | 3.0   | 501  | 0.2229          | 0.7883    | 0.8022 | 0.7952 | 0.9521   |
+| 0.0449        | 4.0   | 668  | 0.2311          | 0.7819    | 0.8116 | 0.7965 | 0.9517   |
+| 0.0449        | 5.0   | 835  | 0.2398          | 0.7853    | 0.7984 | 0.7918 | 0.9504   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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