metadata
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
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.
Model description
This model is a fine-tuned version of 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