
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1368152708
- CO2 Emissions (in grams): 0.0603


## Usage

Python API:

```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch.nn.functional as F

tokenizer = AutoTokenizer.from_pretrained("SalmanFaroz/dark_IntentCLF")
model = AutoModelForSequenceClassification.from_pretrained("SalmanFaroz/dark_IntentCLF")

# Define your input sequence
input_text = "I love AutoTrain"

# Tokenize your input sequence
inputs = tokenizer(input_text, return_tensors="pt")

# Pass the inputs to the model's forward method to get the logits
outputs = model(**inputs)
logits = outputs.logits

# Apply a softmax function to the logits to get the output probabilities
probs = F.softmax(logits, dim=1)

# Convert the tensor of output probabilities to a dictionary
class_probs = {model.config.id2label[i]: prob.item() for i, prob in enumerate(probs[0])}

print(class_probs)

```
{"license": "mit", "tags": ["autotrain", "text-classification"], "datasets": ["SalmanFaroz/dark_IntentCLF"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.0602773344712369}}
transformers pytorch bert text-classification autotrain dataset:SalmanFaroz/dark_IntentCLF license:mit co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #dataset-SalmanFaroz/dark_IntentCLF #license-mit #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1368152708
- CO2 Emissions (in grams): 0.0603


## Usage

Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1368152708
- CO2 Emissions (in grams): 0.0603 ## Usage

Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #dataset-SalmanFaroz/dark_IntentCLF #license-mit #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1368152708
- CO2 Emissions (in grams): 0.0603 ## Usage

Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #dataset-SalmanFaroz/dark_IntentCLF #license-mit #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1368152708
- CO2 Emissions (in grams): 0.0603## Usage

Python API: