---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- regression
model-index:
- name: deberta-v3-small-sp500-edgar-10k-markdown-1024-vN
  results: []
datasets:
- BEE-spoke-data/sp500-edgar-10k-markdown
language:
- en
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# pszemraj/deberta-v3-small-sp500-edgar-10k


this predicts the `ret` column of the training dataset, given the `text` column.



<details>
  
  <summary>Click to expand code example</summary>

```py
import json
from transformers import pipeline
from huggingface_hub import hf_hub_download

model_repo_name = "pszemraj/deberta-v3-small-sp500-edgar-10k"
pipe = pipeline("text-classification", model=model_repo_name)
pipe.tokenizer.model_max_length = 1024

# Download the regression_config.json file
regression_config_path = hf_hub_download(
    repo_id=model_repo_name, filename="regression_config.json"
)
with open(regression_config_path, "r") as f:
    regression_config = json.load(f)


def inverse_scale(prediction, config):
    """apply inverse scaling to a prediction"""
    min_value, max_value = config["min_value"], config["max_value"]
    return prediction * (max_value - min_value) + min_value


def predict_with_pipeline(text, pipe, config, ndigits=4):
    result = pipe(text, truncation=True)[0]  # Get the first (and likely only) result
    score = result["score"] if result["label"] == "LABEL_1" else 1 - result["score"]
    # Apply inverse scaling
    scaled_score = inverse_scale(score, config)
    return round(scaled_score, ndigits)


text = "This is an example text for regression prediction."

# Get predictions
predictions = predict_with_pipeline(text, pipe, regression_config)
print("Predicted returns (frec):", predictions)
```

</details>

## Model description

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on BEE-spoke-data/sp500-edgar-10k-markdown

It achieves the following results on the evaluation set:
- Loss: 0.0005
- Mse: 0.0005


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 30826
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mse    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0064        | 0.54  | 50   | 0.0006          | 0.0006 |
| 0.0043        | 1.08  | 100  | 0.0005          | 0.0005 |
| 0.0028        | 1.61  | 150  | 0.0006          | 0.0006 |
| 0.0025        | 2.15  | 200  | 0.0005          | 0.0005 |
| 0.0025        | 2.69  | 250  | 0.0005          | 0.0005 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2