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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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- recall
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- accuracy
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- precision
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model-index:
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- name: financial_sentiment_model
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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_50agree
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metrics:
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- name: Recall
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type: recall
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value: 0.8839956357328868
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- name: Accuracy
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type: accuracy
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value: 0.8804123711340206
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- name: Precision
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type: precision
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value: 0.8604175202419276
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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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# financial_sentiment_model
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This model is a fine-tuned version of [deepmind/language-perceiver](https://huggingface.co/deepmind/language-perceiver) on the financial_phrasebank dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3467
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- Recall: 0.8840
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- Accuracy: 0.8804
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- Precision: 0.8604
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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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- distributed_type: tpu
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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: 4
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Recall | Accuracy | Precision |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|
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| 0.4481 | 1.0 | 273 | 0.4035 | 0.8526 | 0.8433 | 0.7955 |
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| 0.4069 | 2.0 | 546 | 0.4478 | 0.8683 | 0.8289 | 0.8123 |
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| 0.2225 | 3.0 | 819 | 0.3167 | 0.8747 | 0.8680 | 0.8387 |
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| 0.1245 | 4.0 | 1092 | 0.3467 | 0.8840 | 0.8804 | 0.8604 |
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### Framework versions
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- Transformers 4.15.0
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- Pytorch 1.9.0+cu102
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- Datasets 1.17.0
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- Tokenizers 0.10.3
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