---
tags:
- text-classification
language:
- it
widget:
- text: L utente concede una licenza non esclusiva, trasferibile, sublicenziabile, non soggetta a royalty e valida in tutto il mondo.
co2_eq_emissions:
  emissions: 0.022138627441573373
---

# 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)
```