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license: apache-2.0
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base_model: microsoft/resnet-101
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: Dogs-Breed-Image-Classification-V1
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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config: default
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split: train
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8757971454600668
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Dogs-Breed-Image-Classification-V1
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This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4469
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- Accuracy: 0.8758
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## Model description
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---
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license: apache-2.0
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base_model: microsoft/resnet-101
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: Dogs-Breed-Image-Classification-V1
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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config: default
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split: train
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8757971454600668
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Dogs-Breed-Image-Classification-V1
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This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4469
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- Accuracy: 0.8758
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## Model description
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[Link to the fine-tuned model using resnet-50](https://huggingface.co/jhoppanne/Dogs-Breed-Image-Classification-V0)
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This model was trained using dataset from [Kaggle - Standford dogs dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-dogs-dataset.)
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Quotes from the website:
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The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It was originally collected for fine-grain image categorization, a challenging problem as certain dog breeds have near identical features or differ in colour and age.
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citation:
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Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao and Li Fei-Fei. Novel dataset for Fine-Grained Image Categorization. First Workshop on Fine-Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. [pdf] [poster] [BibTex]
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Secondary:
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J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009. [pdf] [BibTex]
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## Intended uses & limitations
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This model is fined tune solely for classifiying 120 species of dogs.
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## Training and evaluation data
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75% training data, 25% testing data.
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 100
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|
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| No log | 1.0 | 309 | 18.7685 | 0.0091 |
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| 18.7211 | 2.0 | 618 | 18.5975 | 0.0091 |
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| 18.7211 | 3.0 | 927 | 17.4087 | 0.0091 |
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| 15.4274 | 4.0 | 1236 | 11.8712 | 0.0091 |
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| 10.3252 | 5.0 | 1545 | 6.6642 | 0.0091 |
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| 10.3252 | 6.0 | 1854 | 5.2754 | 0.0112 |
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| 6.2268 | 7.0 | 2163 | 4.8454 | 0.0158 |
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| 6.2268 | 8.0 | 2472 | 4.7658 | 0.0140 |
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| 4.9682 | 9.0 | 2781 | 4.6860 | 0.0234 |
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| 4.7245 | 10.0 | 3090 | 4.6165 | 0.0316 |
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| 4.7245 | 11.0 | 3399 | 4.5349 | 0.0446 |
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| 4.5441 | 12.0 | 3708 | 4.4555 | 0.0623 |
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| 4.3912 | 13.0 | 4017 | 4.3437 | 0.0862 |
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| 4.3912 | 14.0 | 4326 | 4.2182 | 0.1330 |
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| 4.2211 | 15.0 | 4635 | 4.0752 | 0.2153 |
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| 4.2211 | 16.0 | 4944 | 3.9803 | 0.2599 |
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| 3.9762 | 17.0 | 5253 | 3.7347 | 0.3596 |
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| 3.69 | 18.0 | 5562 | 3.5493 | 0.4194 |
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| 3.69 | 19.0 | 5871 | 3.3404 | 0.4813 |
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| 3.3803 | 20.0 | 6180 | 3.1122 | 0.5600 |
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| 3.3803 | 21.0 | 6489 | 2.8656 | 0.6101 |
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| 3.0345 | 22.0 | 6798 | 2.6544 | 0.6462 |
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| 2.6793 | 23.0 | 7107 | 2.4178 | 0.6647 |
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| 2.6793 | 24.0 | 7416 | 2.1967 | 0.7121 |
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| 2.3251 | 25.0 | 7725 | 2.0091 | 0.7203 |
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| 1.9975 | 26.0 | 8034 | 1.8189 | 0.7464 |
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| 1.9975 | 27.0 | 8343 | 1.6537 | 0.7519 |
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| 1.7009 | 28.0 | 8652 | 1.4413 | 0.7880 |
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| 1.7009 | 29.0 | 8961 | 1.3137 | 0.7968 |
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| 1.4494 | 30.0 | 9270 | 1.2150 | 0.7929 |
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| 1.2389 | 31.0 | 9579 | 1.1238 | 0.8041 |
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| 1.2389 | 32.0 | 9888 | 1.0215 | 0.8208 |
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| 1.0646 | 33.0 | 10197 | 0.9637 | 0.8190 |
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| 0.9319 | 34.0 | 10506 | 0.8891 | 0.8299 |
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| 0.9319 | 35.0 | 10815 | 0.8520 | 0.8330 |
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| 0.8297 | 36.0 | 11124 | 0.8212 | 0.8400 |
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| 0.8297 | 37.0 | 11433 | 0.7579 | 0.8415 |
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| 0.7293 | 38.0 | 11742 | 0.7254 | 0.8454 |
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| 0.6657 | 39.0 | 12051 | 0.7019 | 0.8457 |
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| 0.6657 | 40.0 | 12360 | 0.6669 | 0.8527 |
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| 0.6047 | 41.0 | 12669 | 0.6510 | 0.8530 |
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| 0.6047 | 42.0 | 12978 | 0.6264 | 0.8545 |
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| 0.557 | 43.0 | 13287 | 0.6275 | 0.8506 |
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| 0.5126 | 44.0 | 13596 | 0.5947 | 0.8536 |
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| 0.5126 | 45.0 | 13905 | 0.5860 | 0.8573 |
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| 0.475 | 46.0 | 14214 | 0.5745 | 0.8545 |
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| 0.4406 | 47.0 | 14523 | 0.5579 | 0.8600 |
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| 0.4406 | 48.0 | 14832 | 0.5386 | 0.8621 |
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| 0.4086 | 49.0 | 15141 | 0.5346 | 0.8624 |
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| 0.4086 | 50.0 | 15450 | 0.5200 | 0.8612 |
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| 0.3882 | 51.0 | 15759 | 0.5233 | 0.8612 |
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| 0.3646 | 52.0 | 16068 | 0.5148 | 0.8640 |
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| 0.3646 | 53.0 | 16377 | 0.5078 | 0.8679 |
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| 0.3386 | 54.0 | 16686 | 0.5067 | 0.8646 |
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| 0.3386 | 55.0 | 16995 | 0.4976 | 0.8673 |
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| 0.3208 | 56.0 | 17304 | 0.4934 | 0.8682 |
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| 0.3039 | 57.0 | 17613 | 0.4849 | 0.8688 |
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| 0.3039 | 58.0 | 17922 | 0.4930 | 0.8691 |
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| 0.2915 | 59.0 | 18231 | 0.4867 | 0.8655 |
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| 0.2784 | 60.0 | 18540 | 0.4832 | 0.8679 |
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| 0.2784 | 61.0 | 18849 | 0.4785 | 0.8670 |
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| 0.2597 | 62.0 | 19158 | 0.4753 | 0.8685 |
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| 0.2597 | 63.0 | 19467 | 0.4701 | 0.8712 |
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| 0.2488 | 64.0 | 19776 | 0.4766 | 0.8697 |
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| 0.2426 | 65.0 | 20085 | 0.4726 | 0.8700 |
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| 0.2426 | 66.0 | 20394 | 0.4670 | 0.8694 |
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| 0.2261 | 67.0 | 20703 | 0.4624 | 0.8722 |
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| 0.2252 | 68.0 | 21012 | 0.4631 | 0.8718 |
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| 0.2252 | 69.0 | 21321 | 0.4702 | 0.8670 |
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| 0.2116 | 70.0 | 21630 | 0.4629 | 0.8715 |
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| 0.2116 | 71.0 | 21939 | 0.4650 | 0.8685 |
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| 0.2032 | 72.0 | 22248 | 0.4670 | 0.8673 |
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| 0.2035 | 73.0 | 22557 | 0.4565 | 0.8670 |
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| 0.2035 | 74.0 | 22866 | 0.4550 | 0.8697 |
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| 0.19 | 75.0 | 23175 | 0.4544 | 0.8706 |
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| 0.19 | 76.0 | 23484 | 0.4483 | 0.8670 |
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| 0.1833 | 77.0 | 23793 | 0.4650 | 0.8694 |
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| 0.184 | 78.0 | 24102 | 0.4604 | 0.8709 |
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| 0.184 | 79.0 | 24411 | 0.4484 | 0.8697 |
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| 0.1728 | 80.0 | 24720 | 0.4469 | 0.8758 |
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| 0.1688 | 81.0 | 25029 | 0.4536 | 0.8676 |
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| 0.1688 | 82.0 | 25338 | 0.4450 | 0.8709 |
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| 0.1674 | 83.0 | 25647 | 0.4530 | 0.8691 |
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| 0.1674 | 84.0 | 25956 | 0.4532 | 0.8725 |
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| 0.1632 | 85.0 | 26265 | 0.4495 | 0.8718 |
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| 0.1605 | 86.0 | 26574 | 0.4440 | 0.8673 |
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| 0.1605 | 87.0 | 26883 | 0.4504 | 0.8731 |
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| 0.1586 | 88.0 | 27192 | 0.4551 | 0.8667 |
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| 0.1558 | 89.0 | 27501 | 0.4498 | 0.8670 |
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| 0.1558 | 90.0 | 27810 | 0.4516 | 0.8718 |
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| 0.1587 | 91.0 | 28119 | 0.4450 | 0.8725 |
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| 0.1587 | 92.0 | 28428 | 0.4435 | 0.8706 |
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| 0.1505 | 93.0 | 28737 | 0.4459 | 0.8722 |
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| 0.1492 | 94.0 | 29046 | 0.4578 | 0.8673 |
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| 0.1492 | 95.0 | 29355 | 0.4499 | 0.8725 |
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| 0.1459 | 96.0 | 29664 | 0.4494 | 0.8703 |
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| 0.1459 | 97.0 | 29973 | 0.4533 | 0.8697 |
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| 0.1481 | 98.0 | 30282 | 0.4524 | 0.8652 |
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| 0.1477 | 99.0 | 30591 | 0.4496 | 0.8715 |
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| 0.1477 | 100.0 | 30900 | 0.4523 | 0.8661 |
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### Framework versions
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- Transformers 4.37.2
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- Pytorch 2.3.0
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- Datasets 2.15.0
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- Tokenizers 0.15.1
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