transformers safetensors gpt2 text-classification en arxiv:1910.09700 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
language:
- en
library_name: transformers
---

# 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]
- **Model type:**  GPT-2 for Sequence Classification
- **Language(s) (NLP):**  English (en)
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** gpt2 by HuggingFace

### Model Sources [optional]

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

## Uses



### Direct Use

This fine-tuned GPT-2 model is intended for sentiment classification of tweets, categorizing them into three sentiment labels.


### Downstream Use [optional]

The model can be fine-tuned further for other text classification tasks.


### Out-of-Scope Use

Not suitable for use cases that require long-context comprehension or real-time sentiment analysis on large datasets


## Bias, Risks, and Limitations



Bias: Since the training data is from a specific dataset (mteb/tweet_sentiment_extraction), the model might exhibit biases inherent to social media data.

Limitations: May not generalize well to non-social media text or languages other than English.





### Recommendations

Be cautious of potential biases when interpreting predictions, especially for sensitive content.

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.

from transformers import GPT2Tokenizer, GPT2ForSequenceClassification

tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2ForSequenceClassification.from_pretrained("your-finetuned-model-id")

inputs = tokenizer("Your text here", return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits


## Training Details

### Training Data

Dataset: mteb/tweet_sentiment_extraction (Tweets labeled with sentiment)

### Training Procedure

Batch Size: 1 (with gradient accumulation steps of 4)
Model: GPT-2
Optimizer: Default settings in HuggingFace Trainer

#### Preprocessing [optional]

[More Information Needed]



Testing Data and Metrics
Testing Dataset: Subset of mteb/tweet_sentiment_extraction
Metrics: Accuracy (using the evaluate library)

#### Training Hyperparameters

Learning Rate: Default for HuggingFace Trainer
Epochs: 

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

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[More Information Needed]

## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

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#### Metrics

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[More Information Needed]

### Results

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#### Summary



## Model Examination [optional]

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[More Information Needed]

## 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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