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--- |
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license: apache-2.0 |
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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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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: medium-base-News_About_Gold |
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results: [] |
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language: |
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- en |
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pipeline_tag: text-classification |
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--- |
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# medium-base-News_About_Gold |
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This model is a fine-tuned version of [funnel-transformer/medium-base](https://huggingface.co/funnel-transformer/medium-base). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2838 |
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- Accuracy: 0.9172 |
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- Weighted f1: 0.9170 |
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- Micro f1: 0.9172 |
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- Macro f1: 0.8854 |
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- Weighted recall: 0.9172 |
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- Micro recall: 0.9172 |
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- Macro recall: 0.8859 |
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- Weighted precision: 0.9171 |
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- Micro precision: 0.9172 |
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- Macro precision: 0.8853 |
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## Model description |
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For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)/News%20About%20Gold%20-%20Sentiment%20Analysis%20-%20Funnel%20with%20W%26B.ipynb |
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This project is part of a comparison of seven (7) transformers. Here is the README page for the comparison: https://github.com/DunnBC22/NLP_Projects/tree/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison) |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-in-commodity-market-gold |
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_Input Word Length:_ |
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![Length of Input Text (in Words)](https://github.com/DunnBC22/NLP_Projects/raw/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)/Images/Input%20Word%20Length.png) |
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_Class Distribution:_ |
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![Length of Input Text (in Words)](https://github.com/DunnBC22/NLP_Projects/raw/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)/Images/Class%20Distribution.png) |
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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: 64 |
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- eval_batch_size: 64 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
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| 0.7426 | 1.0 | 133 | 0.3820 | 0.8803 | 0.8636 | 0.8803 | 0.6690 | 0.8803 | 0.8803 | 0.6809 | 0.8862 | 0.8803 | 0.8992 | |
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| 0.332 | 2.0 | 266 | 0.3083 | 0.9007 | 0.8987 | 0.9007 | 0.8525 | 0.9007 | 0.9007 | 0.8402 | 0.9015 | 0.9007 | 0.8705 | |
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| 0.2381 | 3.0 | 399 | 0.2870 | 0.9106 | 0.9097 | 0.9106 | 0.8686 | 0.9106 | 0.9106 | 0.8539 | 0.9096 | 0.9106 | 0.8862 | |
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| 0.1911 | 4.0 | 532 | 0.2797 | 0.9163 | 0.9158 | 0.9163 | 0.8843 | 0.9163 | 0.9163 | 0.8819 | 0.9159 | 0.9163 | 0.8873 | |
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| 0.1584 | 5.0 | 665 | 0.2838 | 0.9172 | 0.9170 | 0.9172 | 0.8854 | 0.9172 | 0.9172 | 0.8859 | 0.9171 | 0.9172 | 0.8853 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |