---
library_name: transformers
tags: []
---

# Model Card for Model ID

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## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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## Uses
```
from transformers import BertTokenizer, BertForSequenceClassification

# Load the model and tokenizer from the Hugging Face model hub
mymodel = BertForSequenceClassification.from_pretrained("pritam2014/SentimentBERT")
mytokenizer = BertTokenizer.from_pretrained("pritam2014/SentimentBERT",use_auth_token=True)
```
```
def preprocess_text(text):
    # Preprocess the input text
    inputs = mytokenizer.encode_plus(
        text,
        max_length=512,
        padding='max_length',
        truncation=True,
        return_attention_mask=True,
        return_tensors='pt'
    )
    return inputs
```
```
def make_prediction(text):
    # Preprocess the input text
    inputs = preprocess_text(text)

    # Make predictions using the loaded model
    with torch.no_grad():
        outputs = mymodel(inputs['input_ids'], attention_mask=inputs['attention_mask'])
        logits = outputs.logits
        predicted_class_id = torch.argmax(logits).item()

    # Map the predicted class ID to a sentiment label
    sentiment_labels = {0: 'Negative', 1: 'Positive'}
    predicted_sentiment = sentiment_labels[predicted_class_id]

    return predicted_sentiment
```
```
text = "I love this product"
predicted_sentiment = make_prediction(text)
print(predicted_sentiment)
```
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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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