Update README.md

README.md

@@ -1,11 +1,22 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+datasets:
+- Private
+language:
+- en
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+base_model: google-bert/bert-base-uncased
+pipeline_tag: text-classification
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+<!-- This is a fine-tuned BERT model (`bert-base-uncased`) used for classifying text into two categories: **Depression** or **Non-depression**. The model is designed for text classification and has been trained on a custom dataset of mental health-related posts from social media. -->
 
 
 
@@ -13,160 +24,141 @@ tags: []
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+<!-- This model aims to identify signs of depression in written text. It was trained on social media posts labeled as either indicative of depression or not. The model uses the BERT architecture for text classification and was fine-tuned specifically for this task. -->
 
 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]
+- **Developed by:** Ashish Poudel
+- **Model type:** Text Classification
+- **Language(s) (NLP):** English (`en`)
+- **License:** `apache-2.0`
+- **Finetuned from model:** `apache-2.0`
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Sentiment Classifier for Depression](https://huggingface.co/poudel/sentiment-classifier)
+- **Demo [optional]:** [Live Gradio App](https://huggingface.co/spaces/poudel/Sentiment_classifier)
 
 ## 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. -->
+<!--  -->
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+<!-- This model is designed to classify text as either depression-related or non-depression-related. It can be used in social media sentiment analysis, mental health research, and automated text analysis systems. -->
 
-[More Information Needed]
 
 ### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+<!-- The model can be further fine-tuned for other types of sentiment analysis tasks related to mental health. -->
+
 
-[More Information Needed]
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+<!-- The model should not be used for clinical diagnosis or decision-making without the input of medical professionals. It is also unsuitable for text that is not in English or very short/ambiguous inputs. -->
+
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+<!-- The model may suffer from biases inherent in the dataset, such as overrepresenting certain language patterns. It is trained on social media posts, which may not capture all the nuances of real-world conversations about mental health -->
 
-[More Information Needed]
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+<!-- Users should use the model with caution in sensitive applications such as mental health monitoring. It is advised that the model be used alongside professional judgment. -->
 
-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.
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("poudel/sentiment-classifier")
+tokenizer = AutoTokenizer.from_pretrained("poudel/sentiment-classifier")
+
+inputs = tokenizer("I feel hopeless.", return_tensors="pt")
+outputs = model(**inputs)
+predicted_class = torch.argmax(outputs.logits).item()
 
-[More Information Needed]
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+<!-- The model was trained on a custom dataset of tweets labeled as either depression-related or not. Data pre-processing included tokenization and removal of special characters. -->
 
-[More Information Needed]
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+<!-- The model was trained using Hugging Face's transformers library. The training was conducted on a T4 GPU over 3 epochs, with a batch size of 16 and a learning rate of 5e-5. -->
 
-#### Preprocessing [optional]
+#### Preprocessing
 
-[More Information Needed]
+<!-- Text was lowercased, and special characters were removed as well as Tokenization was done using the bert-base-uncased tokenizer.-->
 
 
 #### 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 -->
+- **Training regime:** <!--fp32 -->
+- **Epochs:** <!--3 -->
+- ** Learning rate:** <!--5e-5-->
+- **Batch size:** <!--16 -->
 
-#### Speeds, Sizes, Times [optional]
+#### Speeds, Sizes, Times 
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+<!--Training was conducted for approximately 1 hour on a T4 GPU in Google Colab. -->
 
-[More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
+<!-- The model was evaluated on a 20% holdout set from the custom dataset. -->
 
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+<!-- The model was evaluated using accuracy, precision, recall, and F1 score. -->
 
-[More Information Needed]
 
 ### Results
 
-[More Information Needed]
+Accuracy: 99.87%
+Precision: 99.91%
+Recall: 99.81%
+F1 Score: 99.86% 
 
 #### Summary
+The model achieved high performance across all key metrics, indicating strong predictive capabilities for the text classification task.
 
 
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
+<!-- Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). -->
 
-- **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]
+- **Hardware Type:** [T4 GPU]
+- **Hours used:** [ 1 hour]
+- **Cloud Provider:** [Google Cloud (Colab)]
+- **Carbon Emitted:** [Estimated at 0.45 kg CO2eq]
 
-## Technical Specifications [optional]
+## Technical Specifications [The model uses the BERT (bert-base-uncased) architecture and was fine-tuned for binary classification (depression vs non-depression).]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+[T4 GPU]
 
 #### Software
 
-[More Information Needed]
+[Hugging Face transformers library.]
 
 ## Citation [optional]
 
@@ -174,26 +166,23 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 **BibTeX:**
 
-[More Information Needed]
+[@misc{poudel2024sentimentclassifier,
+  author = {Poudel, Ashish},
+  title = {Sentiment Classifier for Depression},
+  year = {2024},
+  url = {https://huggingface.co/poudel/sentiment-classifier},
+}
+]
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
+[Poudel, A. (2024). Sentiment Classifier for Depression. Retrieved from https://huggingface.co/poudel/sentiment-classifier.]
 
-[More Information Needed]
 
-## Model Card Authors [optional]
+## Model Card Authors 
 
-[More Information Needed]
+[Ashish Poudel]
 
 ## Model Card Contact
 
-[More Information Needed]
+[[email protected]]