---
library_name: transformers
datasets:
- Apk02/Sarcastic_dataset
language:
- en
metrics:
- accuracy
- f1
base_model:
- distilbert/distilbert-base-uncased
pipeline_tag: text-classification
---

# Model Card for Model ID

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## Model Details

### Model Description

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- **Developed by:** Alvin Ray Winston
- **Model type:** Sequence Classification
- **Language(s) (NLP):** EN (English)
- **Finetuned from model:** distilbert/distilbert-base-uncased

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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```
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("AlCyede/sarcastic-text_prediction")
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")

def predict(text):
    inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_id = logits.argmax().item()
    confidence = logits.softmax(dim=1)[0][predicted_class_id].item()
    return {
      "prediction": model.config.id2label[predicted_class_id],
      "confidence": f"{confidence * 100:.02f}%"
    }
```

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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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Datasets: https://huggingface.co/datasets/Apk02/Sarcastic_dataset

### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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Datasets: https://huggingface.co/datasets/Apk02/Sarcastic_dataset

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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]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **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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