End of training

README.md

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+---
+license: apache-2.0
+base_model: distilroberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- recall
+- precision
+- f1
+model-index:
+- name: distilroberta-base-rejection-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. -->
+
+# distilroberta-base-rejection-v1
+
+This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0544
+- Accuracy: 0.9887
+- Recall: 0.9810
+- Precision: 0.9279
+- F1: 0.9537
+
+## 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: 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     |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.0525        | 1.0   | 3536  | 0.0355          | 0.9912   | 0.9583 | 0.9675    | 0.9629 |
+| 0.0219        | 2.0   | 7072  | 0.0312          | 0.9919   | 0.9917 | 0.9434    | 0.9669 |
+| 0.0121        | 3.0   | 10608 | 0.0350          | 0.9939   | 0.9905 | 0.9596    | 0.9748 |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.2+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

distilroberta-base-rejection-v1_emissions.csv

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+2024-01-20T10:29:33,distilroberta-base-rejection-v1_emissions,02c8b68f-b43f-4bb8-8de6-a9c3d5d44194,2502.685886859894,0.07995690783403821,3.1948439176424055e-05,42.5,115.8122138838321,5.787034034729004,0.0295453693439563,0.183039723098328,0.004021413543198922,0.21660650598548323,United States,USA,virginia,,,Linux-5.10.205-195.804.amzn2.x86_64-x86_64-with-glibc2.26,3.10.9,2.3.2,4,AMD EPYC 7R32,1,1 x NVIDIA A10G,-77.4903,39.0469,15.432090759277344,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
+2024-01-20T10:29:27,codecarbon,f49924b3-7f0c-4e83-bbc1-26f795913ce6,2495.7482607364655,0.07987621556153969,3.200491684925353e-05,42.5,173.661481972063,5.787034034729004,0.02946336953573758,0.18291424133127598,0.0040102964797854666,0.21638790734679889,United States,USA,virginia,,,Linux-5.10.205-195.804.amzn2.x86_64-x86_64-with-glibc2.26,3.10.9,2.3.2,4,AMD EPYC 7R32,1,1 x NVIDIA A10G,-77.4903,39.0469,15.432090759277344,machine,N,1.0

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