
# Reviews Sentiment Analysis

A tool that analyzes the overall sentiment of customer reviews for a specific product or service, whether it’s positive or negative. This analysis is performed by using natural language processing algorithms and machine learning from the model ‘Reviews-Sentiment-Analysis’ trained by Kaludi, allowing businesses to gain valuable insights into customer satisfaction and improve their products and services accordingly.

## Training Procedure

- learning_rate = 1e-5
- batch_size = 32
- warmup = 600
- max_seq_length = 128
- num_train_epochs = 10.0

## Validation Metrics

- Loss: 0.159
- Accuracy: 0.952
- Precision: 0.965
- Recall: 0.938
- AUC: 0.988
- F1: 0.951

## 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 don't feel like you trust me to do my job."}' https://api-inference.huggingface.co/models/Kaludi/Reviews-Sentiment-Analysis
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Kaludi/Reviews-Sentiment-Analysis", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Kaludi/Reviews-Sentiment-Analysis", use_auth_token=True)

inputs = tokenizer("I don't feel like you trust me to do my job.", return_tensors="pt")

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["text-classification"], "datasets": ["Kaludi/data-reviews-sentiment-analysis"], "widget": [{"text": "I don't feel like you trust me to do my job.", "example_title": "Negative Example 1"}, {"text": "This service was honestly one of the best I've experienced, I'll definitely come back!", "example_title": "Positive Example 1"}, {"text": "I was extremely disappointed with this product. The quality was terrible and it broke after only a few days of use. Customer service was unhelpful and unresponsive. I would not recommend this product to anyone.", "example_title": "Negative Example 2"}, {"text": "I am so impressed with this product! The quality is outstanding and it has exceeded all of my expectations. The customer service team was also incredibly helpful and responsive to any questions I had. I highly recommend this product to anyone in need of a top-notch, reliable solution.", "example_title": "Positive Example 2"}], "co2_eq_emissions": {"emissions": 24.76716845191504}}
transformers pytorch deberta-v2 text-classification en dataset:Kaludi/data-reviews-sentiment-analysis co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #deberta-v2 #text-classification #en #dataset-Kaludi/data-reviews-sentiment-analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Reviews Sentiment Analysis

A tool that analyzes the overall sentiment of customer reviews for a specific product or service, whether it’s positive or negative. This analysis is performed by using natural language processing algorithms and machine learning from the model ‘Reviews-Sentiment-Analysis’ trained by Kaludi, allowing businesses to gain valuable insights into customer satisfaction and improve their products and services accordingly.

## Training Procedure

- learning_rate = 1e-5
- batch_size = 32
- warmup = 600
- max_seq_length = 128
- num_train_epochs = 10.0

## Validation Metrics

- Loss: 0.159
- Accuracy: 0.952
- Precision: 0.965
- Recall: 0.938
- AUC: 0.988
- F1: 0.951

## Usage

You can use cURL to access this model:



Or Python API:


# Reviews Sentiment Analysis

A tool that analyzes the overall sentiment of customer reviews for a specific product or service, whether it’s positive or negative. This analysis is performed by using natural language processing algorithms and machine learning from the model ‘Reviews-Sentiment-Analysis’ trained by Kaludi, allowing businesses to gain valuable insights into customer satisfaction and improve their products and services accordingly. ## Training Procedure

- learning_rate = 1e-5
- batch_size = 32
- warmup = 600
- max_seq_length = 128
- num_train_epochs = 10.0 ## Validation Metrics

- Loss: 0.159
- Accuracy: 0.952
- Precision: 0.965
- Recall: 0.938
- AUC: 0.988
- F1: 0.951 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #en #dataset-Kaludi/data-reviews-sentiment-analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Reviews Sentiment Analysis

A tool that analyzes the overall sentiment of customer reviews for a specific product or service, whether it’s positive or negative. This analysis is performed by using natural language processing algorithms and machine learning from the model ‘Reviews-Sentiment-Analysis’ trained by Kaludi, allowing businesses to gain valuable insights into customer satisfaction and improve their products and services accordingly. ## Training Procedure

- learning_rate = 1e-5
- batch_size = 32
- warmup = 600
- max_seq_length = 128
- num_train_epochs = 10.0 ## Validation Metrics

- Loss: 0.159
- Accuracy: 0.952
- Precision: 0.965
- Recall: 0.938
- AUC: 0.988
- F1: 0.951 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #en #dataset-Kaludi/data-reviews-sentiment-analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Reviews Sentiment Analysis

A tool that analyzes the overall sentiment of customer reviews for a specific product or service, whether it’s positive or negative. This analysis is performed by using natural language processing algorithms and machine learning from the model ‘Reviews-Sentiment-Analysis’ trained by Kaludi, allowing businesses to gain valuable insights into customer satisfaction and improve their products and services accordingly.## Training Procedure

- learning_rate = 1e-5
- batch_size = 32
- warmup = 600
- max_seq_length = 128
- num_train_epochs = 10.0## Validation Metrics

- Loss: 0.159
- Accuracy: 0.952
- Precision: 0.965
- Recall: 0.938
- AUC: 0.988
- F1: 0.951## Usage

You can use cURL to access this model:



Or Python API: