
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 48941118448
- CO2 Emissions (in grams): 1.7385

## Validation Metrics

- Loss: 0.104
- Accuracy: 0.971
- Macro F1: 0.970
- Micro F1: 0.971
- Weighted F1: 0.971
- Macro Precision: 0.970
- Micro Precision: 0.971
- Weighted Precision: 0.971
- Macro Recall: 0.969
- Micro Recall: 0.971
- Weighted Recall: 0.971


## 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/AyoubChLin/autotrain-longformer-48941118448
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("AyoubChLin/autotrain-longformer-48941118448", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("AyoubChLin/autotrain-longformer-48941118448", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "license": "apache-2.0", "tags": ["autotrain", "text-classification"], "datasets": ["AyoubChLin/autotrain-data-longformer", "SetFit/bbc-news"], "metrics": ["accuracy"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 1.738487465928671}, "pipeline_tag": "text-classification"}
transformers pytorch safetensors longformer text-classification autotrain en dataset:AyoubChLin/autotrain-data-longformer dataset:SetFit/bbc-news license:apache-2.0 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #longformer #text-classification #autotrain #en #dataset-AyoubChLin/autotrain-data-longformer #dataset-SetFit/bbc-news #license-apache-2.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 48941118448
- CO2 Emissions (in grams): 1.7385

## Validation Metrics

- Loss: 0.104
- Accuracy: 0.971
- Macro F1: 0.970
- Micro F1: 0.971
- Weighted F1: 0.971
- Macro Precision: 0.970
- Micro Precision: 0.971
- Weighted Precision: 0.971
- Macro Recall: 0.969
- Micro Recall: 0.971
- Weighted Recall: 0.971


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 48941118448
- CO2 Emissions (in grams): 1.7385 ## Validation Metrics

- Loss: 0.104
- Accuracy: 0.971
- Macro F1: 0.970
- Micro F1: 0.971
- Weighted F1: 0.971
- Macro Precision: 0.970
- Micro Precision: 0.971
- Weighted Precision: 0.971
- Macro Recall: 0.969
- Micro Recall: 0.971
- Weighted Recall: 0.971 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #longformer #text-classification #autotrain #en #dataset-AyoubChLin/autotrain-data-longformer #dataset-SetFit/bbc-news #license-apache-2.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 48941118448
- CO2 Emissions (in grams): 1.7385 ## Validation Metrics

- Loss: 0.104
- Accuracy: 0.971
- Macro F1: 0.970
- Micro F1: 0.971
- Weighted F1: 0.971
- Macro Precision: 0.970
- Micro Precision: 0.971
- Weighted Precision: 0.971
- Macro Recall: 0.969
- Micro Recall: 0.971
- Weighted Recall: 0.971 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #longformer #text-classification #autotrain #en #dataset-AyoubChLin/autotrain-data-longformer #dataset-SetFit/bbc-news #license-apache-2.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 48941118448
- CO2 Emissions (in grams): 1.7385## Validation Metrics

- Loss: 0.104
- Accuracy: 0.971
- Macro F1: 0.970
- Micro F1: 0.971
- Weighted F1: 0.971
- Macro Precision: 0.970
- Micro Precision: 0.971
- Weighted Precision: 0.971
- Macro Recall: 0.969
- Micro Recall: 0.971
- Weighted Recall: 0.971## Usage

You can use cURL to access this model:



Or Python API: