Text Classification
Transformers
PyTorch
English
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use MachineLearningLawyer/conservative-101-rejection-examiner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MachineLearningLawyer/conservative-101-rejection-examiner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MachineLearningLawyer/conservative-101-rejection-examiner")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MachineLearningLawyer/conservative-101-rejection-examiner") model = AutoModelForSequenceClassification.from_pretrained("MachineLearningLawyer/conservative-101-rejection-examiner") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 2952885911
- CO2 Emissions (in grams): 1.7615
Validation Metrics
- Loss: 0.359
- Accuracy: 0.854
- Precision: 0.744
- Recall: 0.296
- AUC: 0.832
- F1: 0.424
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/Awesome7749/autotrain-patent-101-2952885911
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Awesome7749/autotrain-patent-101-2952885911", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Awesome7749/autotrain-patent-101-2952885911", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
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