pytorch bert text-classification unk dataset:ikram54/autotrain-data-harassement transformers autotrain co2_eq_emissions
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ikram54/autotrain-data-harassement
co2_eq_emissions: 2.6332836871905054
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 675420038
- CO2 Emissions (in grams): 2.6332836871905054

## Validation Metrics

- Loss: 0.8747465014457703
- Accuracy: 0.7085201793721974
- Macro F1: 0.579743989078862
- Micro F1: 0.7085201793721974
- Weighted F1: 0.6913786522271296
- Macro Precision: 0.5669375905888698
- Micro Precision: 0.7085201793721974
- Weighted Precision: 0.6760144007300164
- Macro Recall: 0.5940655209452201
- Micro Recall: 0.7085201793721974
- Weighted Recall: 0.7085201793721974


## 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/ikram54/autotrain-harassement-675420038
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("ikram54/autotrain-harassement-675420038", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("ikram54/autotrain-harassement-675420038", use_auth_token=True)

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

outputs = model(**inputs)
```