---
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. -->

# deberta-v3-small-sp500-edgar-10k-markdown-1024-vN


this predicts the `ret` column of the training dataset, given the `text` column. Fine-tuned @ ctx 1024.


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

```py
import json

import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Define the model repository on Hugging Face Hub
model_repo_name = "pszemraj/deberta-v3-small-sp500-edgar-10k"

# Download the regression_config.json file
regression_config_path = hf_hub_download(
    repo_id=model_repo_name, filename="regression_config.json"
)

# Load regression configuration
with open(regression_config_path, "r") as f:
    regression_config = json.load(f)

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_repo_name)
model = AutoModelForSequenceClassification.from_pretrained(model_repo_name)


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


# Example of using the model for inference
def predict(text, tokenizer, model, config, ndigits=4):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        predictions = logits.numpy()
        # Assuming regression task, apply inverse scaling
        scaled_predictions = [inverse_scale(pred[0], config) for pred in predictions]
    return round(scaled_predictions[0], ndigits)


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

# Get predictions
predictions = predict(text, tokenizer, model, regression_config)
print("Predicted Value:", 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