
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 18753417
- CO2 Emissions (in grams): 112.75546781635975

## Validation Metrics

- Loss: 0.9065971970558167
- Accuracy: 0.6680274633512711
- Macro F1: 0.5384854358272774
- Micro F1: 0.6680274633512711
- Weighted F1: 0.6414749238882866
- Macro Precision: 0.6744495173269196
- Micro Precision: 0.6680274633512711
- Weighted Precision: 0.6634090047492259
- Macro Recall: 0.5078466493896978
- Micro Recall: 0.6680274633512711
- Weighted Recall: 0.6680274633512711


## 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/juliensimon/autonlp-song-lyrics-18753417
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("juliensimon/autonlp-song-lyrics-18753417", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("juliensimon/autonlp-song-lyrics-18753417", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": ["autonlp"], "datasets": ["juliensimon/autonlp-data-song-lyrics"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 112.75546781635975}
transformers pytorch safetensors bert text-classification autonlp en dataset:juliensimon/autonlp-data-song-lyrics co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #autonlp #en #dataset-juliensimon/autonlp-data-song-lyrics #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 18753417
- CO2 Emissions (in grams): 112.75546781635975

## Validation Metrics

- Loss: 0.9065971970558167
- Accuracy: 0.6680274633512711
- Macro F1: 0.5384854358272774
- Micro F1: 0.6680274633512711
- Weighted F1: 0.6414749238882866
- Macro Precision: 0.6744495173269196
- Micro Precision: 0.6680274633512711
- Weighted Precision: 0.6634090047492259
- Macro Recall: 0.5078466493896978
- Micro Recall: 0.6680274633512711
- Weighted Recall: 0.6680274633512711


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 18753417
- CO2 Emissions (in grams): 112.75546781635975 ## Validation Metrics

- Loss: 0.9065971970558167
- Accuracy: 0.6680274633512711
- Macro F1: 0.5384854358272774
- Micro F1: 0.6680274633512711
- Weighted F1: 0.6414749238882866
- Macro Precision: 0.6744495173269196
- Micro Precision: 0.6680274633512711
- Weighted Precision: 0.6634090047492259
- Macro Recall: 0.5078466493896978
- Micro Recall: 0.6680274633512711
- Weighted Recall: 0.6680274633512711 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #bert #text-classification #autonlp #en #dataset-juliensimon/autonlp-data-song-lyrics #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 18753417
- CO2 Emissions (in grams): 112.75546781635975 ## Validation Metrics

- Loss: 0.9065971970558167
- Accuracy: 0.6680274633512711
- Macro F1: 0.5384854358272774
- Micro F1: 0.6680274633512711
- Weighted F1: 0.6414749238882866
- Macro Precision: 0.6744495173269196
- Micro Precision: 0.6680274633512711
- Weighted Precision: 0.6634090047492259
- Macro Recall: 0.5078466493896978
- Micro Recall: 0.6680274633512711
- Weighted Recall: 0.6680274633512711 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #safetensors #bert #text-classification #autonlp #en #dataset-juliensimon/autonlp-data-song-lyrics #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 18753417
- CO2 Emissions (in grams): 112.75546781635975## Validation Metrics

- Loss: 0.9065971970558167
- Accuracy: 0.6680274633512711
- Macro F1: 0.5384854358272774
- Micro F1: 0.6680274633512711
- Weighted F1: 0.6414749238882866
- Macro Precision: 0.6744495173269196
- Micro Precision: 0.6680274633512711
- Weighted Precision: 0.6634090047492259
- Macro Recall: 0.5078466493896978
- Micro Recall: 0.6680274633512711
- Weighted Recall: 0.6680274633512711## Usage

You can use cURL to access this model:



Or Python API: