
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 65215136105
- CO2 Emissions (in grams): 0.3308

## Validation Metrics

- Loss: 0.787
- Accuracy: 0.733
- Macro F1: 0.121
- Micro F1: 0.733
- Weighted F1: 0.620
- Macro Precision: 0.105
- Micro Precision: 0.733
- Weighted Precision: 0.537
- Macro Recall: 0.143
- Micro Recall: 0.733
- Weighted Recall: 0.733


## 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/MarketingHHM/autotrain-predictiveoutcomes_transcripts-65215136105
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("MarketingHHM/autotrain-predictiveoutcomes_transcripts-65215136105", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("MarketingHHM/autotrain-predictiveoutcomes_transcripts-65215136105", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["MarketingHHM/autotrain-data-predictiveoutcomes_transcripts"], "widget": [{"text": "I love AutoTrain"}], "co2_eq_emissions": {"emissions": 0.33075628802746376}}
transformers pytorch safetensors distilbert text-classification autotrain en dataset:MarketingHHM/autotrain-data-predictiveoutcomes_transcripts co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #autotrain #en #dataset-MarketingHHM/autotrain-data-predictiveoutcomes_transcripts #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 65215136105
- CO2 Emissions (in grams): 0.3308

## Validation Metrics

- Loss: 0.787
- Accuracy: 0.733
- Macro F1: 0.121
- Micro F1: 0.733
- Weighted F1: 0.620
- Macro Precision: 0.105
- Micro Precision: 0.733
- Weighted Precision: 0.537
- Macro Recall: 0.143
- Micro Recall: 0.733
- Weighted Recall: 0.733


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 65215136105
- CO2 Emissions (in grams): 0.3308 ## Validation Metrics

- Loss: 0.787
- Accuracy: 0.733
- Macro F1: 0.121
- Micro F1: 0.733
- Weighted F1: 0.620
- Macro Precision: 0.105
- Micro Precision: 0.733
- Weighted Precision: 0.537
- Macro Recall: 0.143
- Micro Recall: 0.733
- Weighted Recall: 0.733 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #autotrain #en #dataset-MarketingHHM/autotrain-data-predictiveoutcomes_transcripts #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 65215136105
- CO2 Emissions (in grams): 0.3308 ## Validation Metrics

- Loss: 0.787
- Accuracy: 0.733
- Macro F1: 0.121
- Micro F1: 0.733
- Weighted F1: 0.620
- Macro Precision: 0.105
- Micro Precision: 0.733
- Weighted Precision: 0.537
- Macro Recall: 0.143
- Micro Recall: 0.733
- Weighted Recall: 0.733 ## Usage

You can use cURL to access this model:



Or Python API: