Instructions to use Humayoun/TrOCRTraining2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Humayoun/TrOCRTraining2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Humayoun/TrOCRTraining2")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("Humayoun/TrOCRTraining2") model = AutoModelForImageTextToText.from_pretrained("Humayoun/TrOCRTraining2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Humayoun/TrOCRTraining2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Humayoun/TrOCRTraining2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Humayoun/TrOCRTraining2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Humayoun/TrOCRTraining2
- SGLang
How to use Humayoun/TrOCRTraining2 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Humayoun/TrOCRTraining2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Humayoun/TrOCRTraining2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Humayoun/TrOCRTraining2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Humayoun/TrOCRTraining2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Humayoun/TrOCRTraining2 with Docker Model Runner:
docker model run hf.co/Humayoun/TrOCRTraining2
TrOCRTraining2
This model is a fine-tuned version of microsoft/trocr-base-stage1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4589
- Cer: 0.0115
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.7331 | 1.06 | 50 | 0.8204 | 0.1775 |
| 0.4908 | 2.13 | 100 | 0.5457 | 0.0229 |
| 0.4912 | 3.19 | 150 | 0.5845 | 0.0229 |
| 0.4713 | 4.26 | 200 | 0.5433 | 0.0137 |
| 0.4435 | 5.32 | 250 | 0.4988 | 0.0126 |
| 0.4152 | 6.38 | 300 | 0.5058 | 0.0137 |
| 0.3026 | 7.45 | 350 | 0.4947 | 0.0126 |
| 0.4133 | 8.51 | 400 | 0.4988 | 0.0115 |
| 0.4029 | 9.57 | 450 | 0.4906 | 0.0160 |
| 0.3439 | 10.64 | 500 | 0.4790 | 0.0160 |
| 0.3386 | 11.7 | 550 | 0.4661 | 0.0103 |
| 0.3511 | 12.77 | 600 | 0.4617 | 0.0115 |
| 0.374 | 13.83 | 650 | 0.4629 | 0.0149 |
| 0.3357 | 14.89 | 700 | 0.4589 | 0.0115 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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