
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1230246837
- CO2 Emissions (in grams): 47.6481

## Validation Metrics

- Loss: 0.265
- Accuracy: 0.943
- Macro F1: 0.944
- Micro F1: 0.943
- Weighted F1: 0.943
- Macro Precision: 0.947
- Micro Precision: 0.943
- Weighted Precision: 0.944
- Macro Recall: 0.943
- Micro Recall: 0.943
- Weighted Recall: 0.943

# - Macro F1: 0.944
# - Micro F1: 0.943
# - Macro Precision: 0.947
# - Micro Precision: 0.943
# - Macro Recall: 0.943
# - Micro Recall: 0.943



## 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/jfan-98/autotrain-LegalLong_on_contracts-1230246837
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("jfan-98/autotrain-LegalLong_on_contracts-1230246837", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("jfan-98/autotrain-LegalLong_on_contracts-1230246837", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["jfan-98/autotrain-data-LegalLong_on_contracts"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 47.64808387548789}}
transformers pytorch longformer text-classification autotrain unk dataset:jfan-98/autotrain-data-LegalLong_on_contracts co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #longformer #text-classification #autotrain #unk #dataset-jfan-98/autotrain-data-LegalLong_on_contracts #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1230246837
- CO2 Emissions (in grams): 47.6481

## Validation Metrics

- Loss: 0.265
- Accuracy: 0.943
- Macro F1: 0.944
- Micro F1: 0.943
- Weighted F1: 0.943
- Macro Precision: 0.947
- Micro Precision: 0.943
- Weighted Precision: 0.944
- Macro Recall: 0.943
- Micro Recall: 0.943
- Weighted Recall: 0.943

# - Macro F1: 0.944
# - Micro F1: 0.943
# - Macro Precision: 0.947
# - Micro Precision: 0.943
# - Macro Recall: 0.943
# - Micro Recall: 0.943



## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1230246837
- CO2 Emissions (in grams): 47.6481 ## Validation Metrics

- Loss: 0.265
- Accuracy: 0.943
- Macro F1: 0.944
- Micro F1: 0.943
- Weighted F1: 0.943
- Macro Precision: 0.947
- Micro Precision: 0.943
- Weighted Precision: 0.944
- Macro Recall: 0.943
- Micro Recall: 0.943
- Weighted Recall: 0.943 # - Macro F1: 0.944 # - Micro F1: 0.943 # - Macro Precision: 0.947 # - Micro Precision: 0.943 # - Macro Recall: 0.943 # - Micro Recall: 0.943 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #longformer #text-classification #autotrain #unk #dataset-jfan-98/autotrain-data-LegalLong_on_contracts #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1230246837
- CO2 Emissions (in grams): 47.6481 ## Validation Metrics

- Loss: 0.265
- Accuracy: 0.943
- Macro F1: 0.944
- Micro F1: 0.943
- Weighted F1: 0.943
- Macro Precision: 0.947
- Micro Precision: 0.943
- Weighted Precision: 0.944
- Macro Recall: 0.943
- Micro Recall: 0.943
- Weighted Recall: 0.943 # - Macro F1: 0.944 # - Micro F1: 0.943 # - Macro Precision: 0.947 # - Micro Precision: 0.943 # - Macro Recall: 0.943 # - Micro Recall: 0.943 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #longformer #text-classification #autotrain #unk #dataset-jfan-98/autotrain-data-LegalLong_on_contracts #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1230246837
- CO2 Emissions (in grams): 47.6481## Validation Metrics

- Loss: 0.265
- Accuracy: 0.943
- Macro F1: 0.944
- Micro F1: 0.943
- Weighted F1: 0.943
- Macro Precision: 0.947
- Micro Precision: 0.943
- Weighted Precision: 0.944
- Macro Recall: 0.943
- Micro Recall: 0.943
- Weighted Recall: 0.943# - Macro F1: 0.944# - Micro F1: 0.943# - Macro Precision: 0.947# - Micro Precision: 0.943# - Macro Recall: 0.943# - Micro Recall: 0.943## Usage

You can use cURL to access this model:



Or Python API: