
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 949231426
- CO2 Emissions (in grams): 0.01123534537751425

## Validation Metrics

- Loss: 0.26922607421875
- Accuracy: 1.0
- Macro F1: 1.0
- Micro F1: 1.0
- Weighted F1: 1.0
- Macro Precision: 1.0
- Micro Precision: 1.0
- Weighted Precision: 1.0
- Macro Recall: 1.0
- Micro Recall: 1.0
- Weighted Recall: 1.0


## 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/nitishkumargundapu793/autotrain-chat-bot-responses-949231426
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("nitishkumargundapu793/autotrain-chat-bot-responses-949231426", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("nitishkumargundapu793/autotrain-chat-bot-responses-949231426", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["nitishkumargundapu793/autotrain-data-chat-bot-responses"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.01123534537751425}
transformers pytorch bert text-classification autotrain en dataset:nitishkumargundapu793/autotrain-data-chat-bot-responses co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-nitishkumargundapu793/autotrain-data-chat-bot-responses #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 949231426
- CO2 Emissions (in grams): 0.01123534537751425

## Validation Metrics

- Loss: 0.26922607421875
- Accuracy: 1.0
- Macro F1: 1.0
- Micro F1: 1.0
- Weighted F1: 1.0
- Macro Precision: 1.0
- Micro Precision: 1.0
- Weighted Precision: 1.0
- Macro Recall: 1.0
- Micro Recall: 1.0
- Weighted Recall: 1.0


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 949231426
- CO2 Emissions (in grams): 0.01123534537751425 ## Validation Metrics

- Loss: 0.26922607421875
- Accuracy: 1.0
- Macro F1: 1.0
- Micro F1: 1.0
- Weighted F1: 1.0
- Macro Precision: 1.0
- Micro Precision: 1.0
- Weighted Precision: 1.0
- Macro Recall: 1.0
- Micro Recall: 1.0
- Weighted Recall: 1.0 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-nitishkumargundapu793/autotrain-data-chat-bot-responses #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 949231426
- CO2 Emissions (in grams): 0.01123534537751425 ## Validation Metrics

- Loss: 0.26922607421875
- Accuracy: 1.0
- Macro F1: 1.0
- Micro F1: 1.0
- Weighted F1: 1.0
- Macro Precision: 1.0
- Micro Precision: 1.0
- Weighted Precision: 1.0
- Macro Recall: 1.0
- Micro Recall: 1.0
- Weighted Recall: 1.0 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-nitishkumargundapu793/autotrain-data-chat-bot-responses #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 949231426
- CO2 Emissions (in grams): 0.01123534537751425## Validation Metrics

- Loss: 0.26922607421875
- Accuracy: 1.0
- Macro F1: 1.0
- Micro F1: 1.0
- Weighted F1: 1.0
- Macro Precision: 1.0
- Micro Precision: 1.0
- Weighted Precision: 1.0
- Macro Recall: 1.0
- Micro Recall: 1.0
- Weighted Recall: 1.0## Usage

You can use cURL to access this model:



Or Python API: