Update README.md

README.md

@@ -3,197 +3,86 @@ library_name: transformers
 tags: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for distilgpt2-therapist
 
+This is a fine-tuned GPT-2 model (`distilgpt2`) designed for generating therapist-like responses based on a custom therapy dataset. It can be used to simulate therapeutic dialogues or other text generation tasks in the context of mental health.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-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]
+This model is fine-tuned on the **TherapyDataset**, which contains various therapeutic conversations. The model is intended for text generation tasks related to therapist-style conversations.
 
-### Model Sources [optional]
+- **Model type:** Causal Language Model
+- **Language(s) (NLP):** English
+- **Finetuned from model:** `distilbert/distilgpt2`
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [abishekcodes/distilgpt2-therapist](https://huggingface.co/abishekcodes/distilgpt2-therapist)
 
 ## 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. -->
-
-[More Information Needed]
+This model can be used directly for generating therapist-like responses in a conversational setting or as part of a chatbot system.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can be further fine-tuned for specific therapeutic tasks or integrated into mental health applications that provide guidance and support.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is not intended to replace actual professional therapy. It should not be used for clinical diagnosis or as a substitute for mental health treatment.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model is trained on a specific dataset and may exhibit biases inherent in the dataset. It is not suitable for handling severe mental health issues and should be used with caution.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users should exercise caution while using this model in sensitive contexts. It is not a replacement for professional care, and biases in generated responses should be considered.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[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. -->
-
-[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. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### 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]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[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. -->
-
-[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. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## 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).
-
-- **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
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
+To use the model, install the Hugging Face `transformers` library and load the model with the code below:
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, GPT2LMHeadModel
 
-## Citation [optional]
+tokenizer = AutoTokenizer.from_pretrained("abishekcodes/distilgpt2-therapist")
+model = GPT2LMHeadModel.from_pretrained("abishekcodes/distilgpt2-therapist")
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+inputs = tokenizer("How are you feeling today?", return_tensors="pt")
+outputs = model.generate(inputs['input_ids'], max_length=50)
 
-**BibTeX:**
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-[More Information Needed]
+# Training Details
 
-**APA:**
+## Training Data
 
-[More Information Needed]
+The model was fine-tuned using the **TherapyDataset**, which is publicly available and contains various therapeutic conversations.
 
-## Glossary [optional]
+## Training Procedure
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+- **Training regime:** `fp16` mixed precision  
+- **Batch size:** 6 per device (train and eval)  
+- **Learning rate:** 2e-5  
+- **Number of epochs:** 3  
 
-[More Information Needed]
+## Training Hyperparameters
 
-## More Information [optional]
+- **Training Loss:** 2.006800 → 1.826100  
+- **Validation Loss:** 1.891285 → 1.802560  
 
-[More Information Needed]
+# Evaluation
 
-## Model Card Authors [optional]
+## Testing Data, Factors & Metrics
 
-[More Information Needed]
+The model was evaluated using the test split from the **TherapyDataset**. The evaluation was based on standard text generation metrics.
 
-## Model Card Contact
+## Metrics
 
-[More Information Needed]
+- **Loss during training and validation** was used as the primary metric for evaluation.