negative neutral positive
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ktangri/autotrain-data-financial-sentiment
co2_eq_emissions: 0.007501354635994803
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 765323474
- CO2 Emissions (in grams): 0.007501354635994803

## Validation Metrics

- Loss: 0.0447433702647686
- Accuracy: 0.9823788546255506
- Macro F1: 0.974405452470854
- Micro F1: 0.9823788546255506
- Weighted F1: 0.9823043153179869
- Macro Precision: 0.978208375548801
- Micro Precision: 0.9823788546255506
- Weighted Precision: 0.9823204968555985
- Macro Recall: 0.9707159078140736
- Micro Recall: 0.9823788546255506
- Weighted Recall: 0.9823788546255506


## 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/ktangri/autotrain-financial-sentiment-765323474
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("ktangri/autotrain-financial-sentiment-765323474", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("ktangri/autotrain-financial-sentiment-765323474", use_auth_token=True)

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

outputs = model(**inputs)
```