README.md

---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- zainalq7/autotrain-data-NLU_crypto_sentiment_analysis
co2_eq_emissions: 0.005300030853867218
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 754123133
- CO2 Emissions (in grams): 0.005300030853867218

## Validation Metrics

- Loss: 0.387116938829422
- Accuracy: 0.8658536585365854
- Macro F1: 0.7724053724053724
- Micro F1: 0.8658536585365854
- Weighted F1: 0.8467166979362101
- Macro Precision: 0.8232219717155155
- Micro Precision: 0.8658536585365854
- Weighted Precision: 0.8516026874759421
- Macro Recall: 0.7642089093701996
- Micro Recall: 0.8658536585365854
- Weighted Recall: 0.8658536585365854


## 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/zainalq7/autotrain-NLU_crypto_sentiment_analysis-754123133
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("zainalq7/autotrain-NLU_crypto_sentiment_analysis-754123133", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("zainalq7/autotrain-NLU_crypto_sentiment_analysis-754123133", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)
```