
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 35868888
- CO2 Emissions (in grams): 186.8637425115097

## Validation Metrics

- Loss: 0.2020547091960907
- Accuracy: 0.9233253193796257
- Macro F1: 0.9240407542958707
- Micro F1: 0.9233253193796257
- Weighted F1: 0.921800586774046
- Macro Precision: 0.9432284179846658
- Micro Precision: 0.9233253193796257
- Weighted Precision: 0.9247263361914827
- Macro Recall: 0.9139437626409382
- Micro Recall: 0.9233253193796257
- Weighted Recall: 0.9233253193796257


## 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 AutoNLP"}' https://api-inference.huggingface.co/models/bgoel4132/autonlp-twitter-sentiment-35868888
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("bgoel4132/autonlp-twitter-sentiment-35868888", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("bgoel4132/autonlp-twitter-sentiment-35868888", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autonlp", "datasets": ["bgoel4132/autonlp-data-twitter-sentiment"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 186.8637425115097}
transformers pytorch bert text-classification autonlp en dataset:bgoel4132/autonlp-data-twitter-sentiment co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-bgoel4132/autonlp-data-twitter-sentiment #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 35868888
- CO2 Emissions (in grams): 186.8637425115097

## Validation Metrics

- Loss: 0.2020547091960907
- Accuracy: 0.9233253193796257
- Macro F1: 0.9240407542958707
- Micro F1: 0.9233253193796257
- Weighted F1: 0.921800586774046
- Macro Precision: 0.9432284179846658
- Micro Precision: 0.9233253193796257
- Weighted Precision: 0.9247263361914827
- Macro Recall: 0.9139437626409382
- Micro Recall: 0.9233253193796257
- Weighted Recall: 0.9233253193796257


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 35868888
- CO2 Emissions (in grams): 186.8637425115097 ## Validation Metrics

- Loss: 0.2020547091960907
- Accuracy: 0.9233253193796257
- Macro F1: 0.9240407542958707
- Micro F1: 0.9233253193796257
- Weighted F1: 0.921800586774046
- Macro Precision: 0.9432284179846658
- Micro Precision: 0.9233253193796257
- Weighted Precision: 0.9247263361914827
- Macro Recall: 0.9139437626409382
- Micro Recall: 0.9233253193796257
- Weighted Recall: 0.9233253193796257 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-bgoel4132/autonlp-data-twitter-sentiment #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 35868888
- CO2 Emissions (in grams): 186.8637425115097 ## Validation Metrics

- Loss: 0.2020547091960907
- Accuracy: 0.9233253193796257
- Macro F1: 0.9240407542958707
- Micro F1: 0.9233253193796257
- Weighted F1: 0.921800586774046
- Macro Precision: 0.9432284179846658
- Micro Precision: 0.9233253193796257
- Weighted Precision: 0.9247263361914827
- Macro Recall: 0.9139437626409382
- Micro Recall: 0.9233253193796257
- Weighted Recall: 0.9233253193796257 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-bgoel4132/autonlp-data-twitter-sentiment #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 35868888
- CO2 Emissions (in grams): 186.8637425115097## Validation Metrics

- Loss: 0.2020547091960907
- Accuracy: 0.9233253193796257
- Macro F1: 0.9240407542958707
- Micro F1: 0.9233253193796257
- Weighted F1: 0.921800586774046
- Macro Precision: 0.9432284179846658
- Micro Precision: 0.9233253193796257
- Weighted Precision: 0.9247263361914827
- Macro Recall: 0.9139437626409382
- Micro Recall: 0.9233253193796257
- Weighted Recall: 0.9233253193796257## Usage

You can use cURL to access this model:



Or Python API: