Text Classification
Transformers
PyTorch
Safetensors
English
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use badalsahani/pdf-classification-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use badalsahani/pdf-classification-multi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="badalsahani/pdf-classification-multi")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("badalsahani/pdf-classification-multi") model = AutoModelForSequenceClassification.from_pretrained("badalsahani/pdf-classification-multi") - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: I love AutoTrain 🤗
datasets:
- badalsahani/autotrain-data-pdf-classification
co2_eq_emissions:
emissions: 6.061459826922492
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3447993987
- CO2 Emissions (in grams): 6.0615
Validation Metrics
- Loss: 0.005
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: 1.000
- Macro Recall: 1.000
- Micro Recall: 1.000
- Weighted Recall: 1.000
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/badalsahani/autotrain-pdf-classification-3447993987
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("badalsahani/autotrain-pdf-classification-3447993987", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("badalsahani/autotrain-pdf-classification-3447993987", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)