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)

# pip install -U transformers accelerate
# 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, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

Ranjit0034 commited on Jan 14

Commit

ea16a71

verified ·

1 Parent(s): 1c67c88

Upload scripts/data_pipeline/generate_synthetic.py with huggingface_hub

Browse files

Files changed (1) hide show

scripts/data_pipeline/generate_synthetic.py +22 -4

scripts/data_pipeline/generate_synthetic.py CHANGED Viewed

@@ -1171,18 +1171,36 @@ class BankDatabase:
     """Database of Indian banks."""
     BANKS = [
         Bank("HDFC", "HDFC", "18002586161"),
         Bank("ICICI", "ICIC", "18002662"),
-        Bank("SBI", "SBIN", "1800112211"),
         Bank("Axis", "UTIB", "18004195959"),
         Bank("Kotak", "KKBK", "18601266022"),
-        Bank("PNB", "PUNB", "18001802222"),
-        Bank("BOB", "BARB", "18001024455"),
-        Bank("IDFC", "IDFB", "18001024"),
         Bank("Yes Bank", "YESB", "18001200"),
         Bank("IndusInd", "INDB", "18602677777"),
         Bank("Canara", "CNRB", "18004250018"),
         Bank("Union Bank", "UBIN", "18002082244"),
     ]
     @classmethod

     """Database of Indian banks."""
     BANKS = [
+        # Major Private Banks
         Bank("HDFC", "HDFC", "18002586161"),
         Bank("ICICI", "ICIC", "18002662"),
         Bank("Axis", "UTIB", "18004195959"),
         Bank("Kotak", "KKBK", "18601266022"),
         Bank("Yes Bank", "YESB", "18001200"),
         Bank("IndusInd", "INDB", "18602677777"),
+        Bank("IDFC", "IDFB", "18001024"),
+        Bank("Federal Bank", "FDRL", "18004259259"),
+        Bank("RBL Bank", "RATN", "18004190610"),
+        Bank("Bandhan Bank", "BDBL", "18002589060"),
+        # Major Public Sector Banks
+        Bank("SBI", "SBIN", "1800112211"),
+        Bank("PNB", "PUNB", "18001802222"),
+        Bank("BOB", "BARB", "18001024455"),
         Bank("Canara", "CNRB", "18004250018"),
         Bank("Union Bank", "UBIN", "18002082244"),
+        Bank("Bank of India", "BKID", "18001031906"),
+        Bank("Central Bank", "CBIN", "18001200200"),
+        Bank("Indian Bank", "IDIB", "18001025425"),
+        Bank("UCO Bank", "UCBA", "18001804020"),
+        Bank("IDBI", "IBKL", "18002091234"),
+        # Regional Banks
+        Bank("South Indian Bank", "SIBL", "18004259366"),
+        Bank("Karur Vysya", "KVBL", "18002002062"),
+        Bank("City Union Bank", "CIUB", "18002000456"),
+        Bank("Tamilnad Mercantile", "TMBL", "18001234465"),
+        Bank("Karnataka Bank", "KARB", "18005201444"),
     ]
     @classmethod