# Load model directly
from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering
processor = AutoProcessor.from_pretrained("Tejagoud/bardydatasample_model")
model = AutoModelForDocumentQuestionAnswering.from_pretrained("Tejagoud/bardydatasample_model")Quick Links
bardydatasample_model
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.9303
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.6435 | 5.0 | 50 | 3.6260 |
| 3.7015 | 10.0 | 100 | 3.3646 |
| 3.4139 | 15.0 | 150 | 3.2407 |
| 3.3301 | 20.0 | 200 | 3.9303 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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Model tree for Tejagoud/bardydatasample_model
Base model
microsoft/layoutlmv2-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="Tejagoud/bardydatasample_model")