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README.md
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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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datasets:
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- financial_phrasebank
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metrics:
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- accuracy
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- f1
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model-index:
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- name: distilbert-base-uncased_allagree3
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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: 0.9778761061946902
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- name: F1
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type: f1
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value: 0.9780006392634297
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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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# distilbert-base-uncased_allagree3
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the financial_phrasebank dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0937
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- Accuracy: 0.9779
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- F1: 0.9780
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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: 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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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| 0.6418 | 1.0 | 57 | 0.3340 | 0.8805 | 0.8768 |
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| 0.1821 | 2.0 | 114 | 0.1088 | 0.9690 | 0.9691 |
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| 0.0795 | 3.0 | 171 | 0.0822 | 0.9823 | 0.9823 |
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| 0.0385 | 4.0 | 228 | 0.0939 | 0.9646 | 0.9646 |
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| 0.0218 | 5.0 | 285 | 0.1151 | 0.9735 | 0.9737 |
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| 0.0149 | 6.0 | 342 | 0.1126 | 0.9690 | 0.9694 |
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| 0.006 | 7.0 | 399 | 0.0989 | 0.9779 | 0.9780 |
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| 0.0093 | 8.0 | 456 | 0.1009 | 0.9779 | 0.9780 |
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| 0.0063 | 9.0 | 513 | 0.0899 | 0.9779 | 0.9780 |
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| 0.0039 | 10.0 | 570 | 0.0937 | 0.9779 | 0.9780 |
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### Framework versions
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- Transformers 4.17.0
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- Pytorch 1.11.0+cpu
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- Datasets 2.3.2
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- Tokenizers 0.12.1
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