Text Classification
Transformers
PyTorch
Chinese
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use 0x-YuAN/Non_CL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 0x-YuAN/Non_CL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="0x-YuAN/Non_CL")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("0x-YuAN/Non_CL") model = AutoModelForSequenceClassification.from_pretrained("0x-YuAN/Non_CL") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1256348072
- CO2 Emissions (in grams): 8.6679
Validation Metrics
- Loss: 0.065
- Accuracy: 0.986
- Macro F1: 0.972
- Micro F1: 0.986
- Weighted F1: 0.986
- Macro Precision: 0.973
- Micro Precision: 0.986
- Weighted Precision: 0.986
- Macro Recall: 0.971
- Micro Recall: 0.986
- Weighted Recall: 0.986
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/yuan1729/autotrain-laws_1-1256348072
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("yuan1729/autotrain-laws_1-1256348072", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("yuan1729/autotrain-laws_1-1256348072", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
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