Instructions to use kolkata97/pe-llm-0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kolkata97/pe-llm-0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kolkata97/pe-llm-0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kolkata97/pe-llm-0") model = AutoModelForSequenceClassification.from_pretrained("kolkata97/pe-llm-0") - Notebooks
- Google Colab
- Kaggle
Model Trained
- Problem type: Multi-class Classification
- Model ID: pe-llm-0.1
- CO2 Emissions (in grams): 0.0221
Validation Metrics
- Loss: 0.841
- Accuracy: 0.761
- Macro F1: 0.644
- Micro F1: 0.761
- Weighted F1: 0.750
- Macro Precision: 0.679
- Micro Precision: 0.761
- Weighted Precision: 0.748
- Macro Recall: 0.635
- Micro Recall: 0.761
- Weighted Recall: 0.761
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/kolkata97/autotrain-pe-llm-0.6-89942144050
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("kolkata97/autotrain-pe-llm-0.6-89942144050", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("kolkata97/autotrain-pe-llm-0.6-89942144050", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
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