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jasmeeetsingh
/
twitter-depression-classification-sentiment140

Text Classification
Transformers
PyTorch
English
roberta
twitter
depression
sentiment140
Inference Endpoints
Model card Files Community
twitter-depression-classification-sentiment140
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---
datasets:
- sentiment140
metrics:
- f1
license: apache-2.0
language:
- en
pipeline_tag: text-classification
tags:
- twitter
- depression
- sentiment140
---
# Model Card for Model ID

jasmeeetsingh/twitter-depression-classification-sentiment140 is a deep learning model trained to classify whether a given tweet is related to depression or not. 
The model is based on a transformer architecture and fine-tuned on a large corpus of tweets annotated as depressive or non-depressive.
## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** Jasmeet Singh Sandhu
- **Finetuned from model [optional]:** paulagarciaserrano/roberta-depression-detection

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
The model is intended to be used to automatically classify tweets as depressive or non-depressive. 
It can be used to analyze large volumes of tweets and identify users who may be at risk of depression, as well as to monitor the prevalence of depression-related discussions on social media platforms.


## 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]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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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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## Glossary [optional]

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