Update README.md

README.md

@@ -43,6 +43,8 @@ Model can accurately recognize emotions classes- Angry,Sad,Fearful,Happy,Disgust
 Emotions recognized - Angry,Sad,Fearful,Happy,Disgusted,Surprised,Calm with ~80% accuracy.
 
 Use the code below to get started with the model:
+
+
 class MultimodalModel(nn.Module):
     '''
     Custom PyTorch model that takes as input both the audio features and the text embeddings, and concatenates the last hidden states from the Hubert and BERT models.
@@ -66,131 +68,38 @@ class MultimodalModel(nn.Module):
         logits = self.classifier(concat_output)
         return logits
 
-[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
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
 
-## Glossary [optional]
+        def load_model():
+    """
+    Load and configure various models and tokenizers for a multi-modal application.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+    This function loads a multi-modal model and its weights from a specified source,
+    initializes tokenizers for the model and an additional language model, and returns
+    these components for use in a multi-modal application.
 
-[More Information Needed]
+    Returns:
+        tuple: A tuple containing the following components:
+            - multiModel (MultimodalModel): The multi-modal model.
+            - tokenizer (AutoTokenizer): Tokenizer for the multi-modal model.
+            - model_gpt (AutoModelForCausalLM): Language model for text generation.
+            - tokenizer_gpt (AutoTokenizer): Tokenizer for the language model.
+    """
+    # Load the model
+    multiModel = MultimodalModel(bert_model_name, num_labels)
 
-## More Information [optional]
+    # Load the model weights and tokenizer directly from Hugging Face Spaces
+    multiModel.load_state_dict(torch.hub.load_state_dict_from_url(model_weights_path, map_location=device), strict=False)
+    tokenizer = AutoTokenizer.from_pretrained("netgvarun2005/MultiModalBertHubertTokenizer") 
 
-[More Information Needed]
+    # GenAI
+    tokenizer_gpt = AutoTokenizer.from_pretrained("netgvarun2005/GPTTherapistDeepSpeedTokenizer", pad_token='<|pad|>',bos_token='<|startoftext|>',eos_token='<|endoftext|>')
+    model_gpt = AutoModelForCausalLM.from_pretrained("netgvarun2005/GPTTherapistDeepSpeedModel")
+   
+    return multiModel,tokenizer,model_gpt,tokenizer_gpt
 
-## Model Card Authors [optional]
 
-[More Information Needed]
 
-## Model Card Contact
 
-[More Information Needed]
+## Model Card Authors [Varun Sharma]