Instructions to use deepaksiloka/autotrain-name_classification-1685059436 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepaksiloka/autotrain-name_classification-1685059436 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="deepaksiloka/autotrain-name_classification-1685059436")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("deepaksiloka/autotrain-name_classification-1685059436", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:Config file config.json cannot be fetched (too big)
Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)
Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1685059436
- CO2 Emissions (in grams): 0.3858
Validation Metrics
- Loss: 0.063
- Accuracy: 0.988
- Precision: 0.989
- Recall: 0.989
- F1: 0.989
Usage
You can use cURL to access this model:
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/deepaksiloka/autotrain-name_classification-1685059436
Or Python API:
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("deepaksiloka/autotrain-name_classification-1685059436", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("deepaksiloka/autotrain-name_classification-1685059436", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
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