---
library_name: transformers
datasets:
- 4DR1455/finance_questions
language:
- en
base_model:
- google-t5/t5-small
---

# Model Card for Model ID

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This model is fine-tuned on google colab using the L4 GPU.

## Model Details

### Model Description

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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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### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

[4DR1455/finance_questions](https://huggingface.co./datasets/4DR1455/finance_questions)

Training data used 60% of the data.

### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters


These are the arguments used for fine-tuning:

```
model_args = ModelArguments(
    model_name="google-t5/t5-small",
    use_4bit=True,
    use_nested_quant=True,
    bnb_4bit_compute_dtype="bfloat16",
    bnb_4bit_quant_dtype="nf4",
    lora_alpha=128,
    lora_dropout=0.1,
    lora_r=8
)

script_args = ScriptArguments(
    per_device_train_batch_size=64,
    per_device_eval_batch_size=64,
    # auto_find_batch_size=True,
    gradient_accumulation_steps=4,
    learning_rate=2e-5,
    weight_decay=0.01,
    max_seq_length=512,
    dataset_name="4DR1455/finance_questions",
    bf16=True,
    optim='adafactor',
    lr_scheduler_type='cosine',
    packing=True,
    num_train_epochs=3,
    save_steps=20,
    logging_steps=10,
    eval_steps=20,
    warmup_steps=500,
    eval_strategy='steps',
    run_name="finetuning-llm-3",
    report_to="wandb",
    save_safetensors=False,
    label_names=['labels'],
    load_best_model_at_end=True,
    dataloader_num_workers=10,
)
```


#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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