End of training

README.md

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+---
+license: mit
+base_model: microsoft/deberta-v3-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- recall
+- precision
+- f1
+model-index:
+- name: deberta-v3-base-prompt-injection-v2-2024-04-20-16-52
+  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-base-prompt-injection-v2-2024-04-20-16-52
+
+This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0036
+- Accuracy: 0.9993
+- Recall: 0.9994
+- Precision: 0.9992
+- F1: 0.9993
+
+## 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: 32
+- eval_batch_size: 64
+- seed: 49994
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.06
+- num_epochs: 3
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Recall | Precision | F1     |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.0079        | 1.0   | 7711  | 0.0052          | 0.9988   | 0.9982 | 0.9994    | 0.9988 |
+| 0.0026        | 2.0   | 15422 | 0.0052          | 0.9987   | 0.9988 | 0.9987    | 0.9988 |
+| 0.0004        | 3.0   | 23133 | 0.0063          | 0.9990   | 0.9989 | 0.9992    | 0.9990 |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 2.2.2+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

deberta-v3-base-prompt-injection-v2_emissions.csv

@@ -0,0 +1,2 @@
+timestamp,project_name,run_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,on_cloud,pue
+2024-04-21T01:05:57,deberta-v3-base-prompt-injection-v2_emissions,be24c49c-34fd-4330-8fae-045ee195f602,29613.912818193436,0.7573653175770764,2.557464534412304e-05,42.5,66.53002163649536,5.78702974319458,0.34960826874391937,1.6545413322431202,0.04758375938084103,2.051733360367884,United States,USA,virginia,,,Linux-5.10.213-201.855.amzn2.x86_64-x86_64-with-glibc2.26,3.10.9,2.3.5,4,AMD EPYC 7R32,1,1 x NVIDIA A10G,-77.2481,38.6583,15.432079315185547,machine,N,1.0