
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 5521155

## Validation Metrics

- Loss: 1.3173143863677979
- Accuracy: 0.8220706757594545
- Macro F1: 0.5713688384455807
- Micro F1: 0.8220706757594544
- Weighted F1: 0.8217158913702755
- Macro Precision: 0.6064387992817253
- Micro Precision: 0.8220706757594545
- Weighted Precision: 0.8491515834140735
- Macro Recall: 0.5873349311175117
- Micro Recall: 0.8220706757594545
- Weighted Recall: 0.8220706757594545


## 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/mgrella/autonlp-bank-transaction-classification-5521155
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("mgrella/autonlp-bank-transaction-classification-5521155", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("mgrella/autonlp-bank-transaction-classification-5521155", use_auth_token=True)

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

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


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 5521155

## Validation Metrics

- Loss: 1.3173143863677979
- Accuracy: 0.8220706757594545
- Macro F1: 0.5713688384455807
- Micro F1: 0.8220706757594544
- Weighted F1: 0.8217158913702755
- Macro Precision: 0.6064387992817253
- Micro Precision: 0.8220706757594545
- Weighted Precision: 0.8491515834140735
- Macro Recall: 0.5873349311175117
- Micro Recall: 0.8220706757594545
- Weighted Recall: 0.8220706757594545


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 5521155 ## Validation Metrics

- Loss: 1.3173143863677979
- Accuracy: 0.8220706757594545
- Macro F1: 0.5713688384455807
- Micro F1: 0.8220706757594544
- Weighted F1: 0.8217158913702755
- Macro Precision: 0.6064387992817253
- Micro Precision: 0.8220706757594545
- Weighted Precision: 0.8491515834140735
- Macro Recall: 0.5873349311175117
- Micro Recall: 0.8220706757594545
- Weighted Recall: 0.8220706757594545 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #bert #text-classification #autonlp #it #dataset-mgrella/autonlp-data-bank-transaction-classification #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 5521155 ## Validation Metrics

- Loss: 1.3173143863677979
- Accuracy: 0.8220706757594545
- Macro F1: 0.5713688384455807
- Micro F1: 0.8220706757594544
- Weighted F1: 0.8217158913702755
- Macro Precision: 0.6064387992817253
- Micro Precision: 0.8220706757594545
- Weighted Precision: 0.8491515834140735
- Macro Recall: 0.5873349311175117
- Micro Recall: 0.8220706757594545
- Weighted Recall: 0.8220706757594545 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #safetensors #bert #text-classification #autonlp #it #dataset-mgrella/autonlp-data-bank-transaction-classification #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 5521155## Validation Metrics

- Loss: 1.3173143863677979
- Accuracy: 0.8220706757594545
- Macro F1: 0.5713688384455807
- Micro F1: 0.8220706757594544
- Weighted F1: 0.8217158913702755
- Macro Precision: 0.6064387992817253
- Micro Precision: 0.8220706757594545
- Weighted Precision: 0.8491515834140735
- Macro Recall: 0.5873349311175117
- Micro Recall: 0.8220706757594545
- Weighted Recall: 0.8220706757594545## Usage

You can use cURL to access this model:



Or Python API: