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--- |
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language: |
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- ko |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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widget: |
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- text: 이 회사는 러시아의 톰스크 지역에 있는 베니어 공장에 기계를 납품하기로 되어 있었다. |
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example_title: example01 |
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- text: 새로운 생산공장으로 인해 회사는 예상되는 수요 증가를 충족시킬 수 있는 능력을 증가시키고 원자재 사용을 개선하여 생산 수익성을 높일 |
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것이다. |
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example_title: example02 |
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- text: 국제 전자산업 회사인 엘코텍은 탈린 공장에서 수십 명의 직원을 해고했으며, 이전의 해고와는 달리 회사는 사무직 직원 수를 줄였다고 일간 |
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포스티메스가 보도했다. |
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example_title: example03 |
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base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment |
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model-index: |
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- name: ko-finance_news_classifier |
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results: [] |
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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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# ko-finance_news_classifier |
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This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4474 |
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- Accuracy: 0.8423 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation 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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 20 |
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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 | 243 | 1.0782 | 0.8010 | |
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| No log | 2.0 | 486 | 1.0328 | 0.8381 | |
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| 0.0766 | 3.0 | 729 | 1.2348 | 0.8330 | |
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| 0.0766 | 4.0 | 972 | 1.3915 | 0.8052 | |
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| 0.046 | 5.0 | 1215 | 1.2995 | 0.8474 | |
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| 0.046 | 6.0 | 1458 | 1.2926 | 0.8361 | |
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| 0.0512 | 7.0 | 1701 | 1.2889 | 0.8330 | |
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| 0.0512 | 8.0 | 1944 | 1.3107 | 0.8392 | |
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| 0.0415 | 9.0 | 2187 | 1.4514 | 0.8309 | |
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| 0.0415 | 10.0 | 2430 | 1.2869 | 0.8381 | |
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| 0.0279 | 11.0 | 2673 | 1.2874 | 0.8526 | |
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| 0.0279 | 12.0 | 2916 | 1.4731 | 0.8423 | |
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| 0.0126 | 13.0 | 3159 | 1.3956 | 0.8443 | |
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| 0.0126 | 14.0 | 3402 | 1.4211 | 0.8454 | |
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| 0.0101 | 15.0 | 3645 | 1.3686 | 0.8474 | |
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| 0.0101 | 16.0 | 3888 | 1.4412 | 0.8423 | |
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| 0.0114 | 17.0 | 4131 | 1.4376 | 0.8423 | |
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| 0.0114 | 18.0 | 4374 | 1.4566 | 0.8423 | |
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| 0.0055 | 19.0 | 4617 | 1.4439 | 0.8443 | |
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| 0.0055 | 20.0 | 4860 | 1.4474 | 0.8423 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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