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--- |
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license: apache-2.0 |
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thumbnail: https://huggingface.co/mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis/resolve/main/logo_no_bg.png |
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tags: |
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- generated_from_trainer |
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- financial |
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- stocks |
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- sentiment |
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widget: |
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- text: "Operating profit totaled EUR 9.4 mn , down from EUR 11.7 mn in 2004 ." |
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- text: "Dunder mifflin Operating profit totaled EUR 9.4 mn , down from EUR 11.7 mn in 2004 ." |
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datasets: |
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- financial_phrasebank |
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metrics: |
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- accuracy |
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model-index: |
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- name: distilRoberta-financial-sentiment |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: financial_phrasebank |
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type: financial_phrasebank |
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args: sentences_allagree |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9923008849557522 |
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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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<div style="text-align:center;width:250px;height:250px;"> |
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<img src="https://huggingface.co/mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis/resolve/main/logo_no_bg.png" alt="logo"> |
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</div> |
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# DistilRoberta-financial-sentiment |
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This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the financial_phrasebank dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1116 |
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- Accuracy: **0.99**23 |
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## Base Model description |
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This model is a distilled version of the [RoBERTa-base model](https://huggingface.co/roberta-base). It follows the same training procedure as [DistilBERT](https://huggingface.co/distilbert-base-uncased). |
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The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/master/examples/distillation). |
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This model is case-sensitive: it makes a difference between English and English. |
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The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base). |
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On average DistilRoBERTa is twice as fast as Roberta-base. |
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## Training Data |
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Polar sentiment dataset of sentences from financial news. The dataset consists of 4840 sentences from English language financial news categorised by sentiment. The dataset is divided by agreement rate of 5-8 annotators. |
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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: 8 |
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- eval_batch_size: 8 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 255 | 0.1670 | 0.9646 | |
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| 0.209 | 2.0 | 510 | 0.2290 | 0.9558 | |
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| 0.209 | 3.0 | 765 | 0.2044 | 0.9558 | |
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| 0.0326 | 4.0 | 1020 | 0.1116 | 0.9823 | |
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| 0.0326 | 5.0 | 1275 | 0.1127 | 0.9779 | |
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### Framework versions |
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- Transformers 4.10.2 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.12.1 |
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- Tokenizers 0.10.3 |
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