Instructions to use Ranjit0034/finance-entity-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ranjit0034/finance-entity-extractor with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Ranjit0034/finance-entity-extractor", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ranjit0034/finance-entity-extractor", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Ranjit0034/finance-entity-extractor", trust_remote_code=True)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Ranjit0034/finance-entity-extractor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ranjit0034/finance-entity-extractor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ranjit0034/finance-entity-extractor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Ranjit0034/finance-entity-extractor

SGLang

How to use Ranjit0034/finance-entity-extractor 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 "Ranjit0034/finance-entity-extractor" \
    --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": "Ranjit0034/finance-entity-extractor",
		"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 "Ranjit0034/finance-entity-extractor" \
        --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": "Ranjit0034/finance-entity-extractor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Ranjit0034/finance-entity-extractor with Docker Model Runner:
```
docker model run hf.co/Ranjit0034/finance-entity-extractor
```

finance-entity-extractor / evaluation_results_v6.json

Ranjit Behera

FinEE v1.0 - Finance Entity Extractor

dcc24f8 5 months ago

raw

history blame contribute delete

1.03 kB

	{
	"model": "models/base/phi3-finance-base",
	"adapter": "models/adapters/finance-lora-v6",
	"total_samples": 20,
	"overall_accuracy": 60.0,
	"by_bank": {
	"paytm": {
	"correct": 0,
	"total": 4
	},
	"kotak": {
	"correct": 1,
	"total": 2
	},
	"axis": {
	"correct": 3,
	"total": 5
	},
	"sbi": {
	"correct": 1,
	"total": 2
	},
	"hdfc": {
	"correct": 2,
	"total": 2
	},
	"gpay": {
	"correct": 1,
	"total": 1
	},
	"icici": {
	"correct": 3,
	"total": 3
	},
	"phonepe": {
	"correct": 1,
	"total": 1
	}
	},
	"by_field": {
	"amount": {
	"correct": 20,
	"total": 20
	},
	"type": {
	"correct": 20,
	"total": 20
	},
	"date": {
	"correct": 19,
	"total": 20
	},
	"account": {
	"correct": 17,
	"total": 20
	},
	"reference": {
	"correct": 15,
	"total": 20
	},
	"merchant": {
	"correct": 12,
	"total": 12
	}
	}
	}