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-v1
+  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-v1
+
+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: 0.0010
+- Accuracy: 0.9999
+- Recall: 0.9997
+- Precision: 0.9998
+- F1: 0.9998
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step   | Validation Loss | Accuracy | Recall | Precision | F1     |
+|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.0038        | 1.0   | 36130  | 0.0026          | 0.9998   | 0.9994 | 0.9992    | 0.9993 |
+| 0.0001        | 2.0   | 72260  | 0.0021          | 0.9998   | 0.9997 | 0.9989    | 0.9993 |
+| 0.0           | 3.0   | 108390 | 0.0015          | 0.9999   | 0.9997 | 0.9995    | 0.9996 |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.1+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0

deberta-v3-base-prompt-injection-v1_emissions.csv

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+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
+2023-11-25T17:36:56,deberta-v3-base-prompt-injection-v1_emissions,a3c7db83-094a-4990-b287-8b2287213c94,33721.252032995224,0.9992452046508414,2.9632506043165606e-05,42.5,62.06199585789132,5.787036895751953,0.39809487054447334,2.25471590377128,0.05418500960607557,2.706995783921832,United States,USA,virginia,,,Linux-5.10.198-187.748.amzn2.x86_64-x86_64-with-glibc2.26,3.10.13,2.3.1,4,AMD EPYC 7R32,1,1 x NVIDIA A10G,-77.4903,39.0469,15.432098388671875,machine,N,1.0

emissions.csv

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+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
+2023-11-25T17:36:39,codecarbon,eb0935eb-df8e-40ce-bfb9-217b4447d621,33705.04844260216,0.9990662916168788,2.9641443575381107e-05,42.5,182.2944905268953,5.787036895751953,0.3979035085568824,2.254447534389918,0.05416005831018278,2.706511101256985,United States,USA,virginia,,,Linux-5.10.198-187.748.amzn2.x86_64-x86_64-with-glibc2.26,3.10.13,2.3.1,4,AMD EPYC 7R32,1,1 x NVIDIA A10G,-77.4903,39.0469,15.432098388671875,machine,N,1.0