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--- |
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library_name: transformers |
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tags: |
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- sentiment |
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- classifier |
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license: mit |
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datasets: |
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- financial_phrasebank |
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language: |
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- en |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** Mit Patel |
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- **Model type:** Text generation/ classifier |
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- **Language(s) (NLP):** English |
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- **Finetuned from model :** Phi-2 |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the 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: 0.0002 |
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- train_batch_size: 4 |
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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: cosine |
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- training_steps: 10000 |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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### Results |
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[More Information Needed] |
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#### Summary |
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### Framework versions |
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- PEFT 0.8.2 |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
