

## Validation Metrics

- Loss: 0.595
- Accuracy: 0.789
- Macro F1: 0.575
- Micro F1: 0.789
- Weighted F1: 0.763
- Macro Precision: 0.630
- Micro Precision: 0.789
- Weighted Precision: 0.775
- Macro Recall: 0.588
- Micro Recall: 0.789
- Weighted Recall: 0.789


## 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 this product! One of my best purchases this year."}' https://api-inference.huggingface.co/models/madmancity/revmlc
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("madmancity/revmlc", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("madmancity/revmlc", use_auth_token=True)

inputs = tokenizer("I love this product! One of my best purchases this year.", return_tensors="pt")

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["text-classification", "sentiment-analysis"], "datasets": ["madmancity/revmlc"], "widget": [{"text": "I love this product! One of my best purchases this year."}]}
transformers pytorch deberta-v2 text-classification sentiment-analysis en dataset:madmancity/revmlc autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #text-classification #sentiment-analysis #en #dataset-madmancity/revmlc #autotrain_compatible #endpoints_compatible #region-us 



## Validation Metrics

- Loss: 0.595
- Accuracy: 0.789
- Macro F1: 0.575
- Micro F1: 0.789
- Weighted F1: 0.763
- Macro Precision: 0.630
- Micro Precision: 0.789
- Weighted Precision: 0.775
- Macro Recall: 0.588
- Micro Recall: 0.789
- Weighted Recall: 0.789


## Usage

You can use cURL to access this model:



Or Python API:


## Validation Metrics

- Loss: 0.595
- Accuracy: 0.789
- Macro F1: 0.575
- Micro F1: 0.789
- Weighted F1: 0.763
- Macro Precision: 0.630
- Micro Precision: 0.789
- Weighted Precision: 0.775
- Macro Recall: 0.588
- Micro Recall: 0.789
- Weighted Recall: 0.789 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #sentiment-analysis #en #dataset-madmancity/revmlc #autotrain_compatible #endpoints_compatible #region-us 
 ## Validation Metrics

- Loss: 0.595
- Accuracy: 0.789
- Macro F1: 0.575
- Micro F1: 0.789
- Weighted F1: 0.763
- Macro Precision: 0.630
- Micro Precision: 0.789
- Weighted Precision: 0.775
- Macro Recall: 0.588
- Micro Recall: 0.789
- Weighted Recall: 0.789 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #sentiment-analysis #en #dataset-madmancity/revmlc #autotrain_compatible #endpoints_compatible #region-us 
## Validation Metrics

- Loss: 0.595
- Accuracy: 0.789
- Macro F1: 0.575
- Micro F1: 0.789
- Weighted F1: 0.763
- Macro Precision: 0.630
- Micro Precision: 0.789
- Weighted Precision: 0.775
- Macro Recall: 0.588
- Micro Recall: 0.789
- Weighted Recall: 0.789## Usage

You can use cURL to access this model:



Or Python API: