Update README.md

README.md

@@ -1,199 +1,101 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- emotion
+- classification
+- roberta
+- multi-label
+- sentiment-analysis
+license: mit
+language:
+- en
+pipeline_tag: text-classification
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
 ## 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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## 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. -->
+This is a finetuned roberta-base model aimed at identifying the strength of emotions for an input comment. 
 
-### Direct Use
+### Downstream Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[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 -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+Embeddings for comments can be extracted for downstream analyses
 
 ## Bias, Risks, and Limitations
+Sarcasm is treated as the combination of "amusement" and "disapproval" amusement can apply to irony and humorous tone, but largely appleis to sarcasm... adding specific class for sarcasm is a much needed improvement that will be pursued later down the line
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### 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.
+not many risks... just MANY limitations. The training dataset was initially imbalanced, this was remedied with data augmentation and a weighted loss function... nontheless it struggles with sarcasm and sometimes unpredictable predictions because of dominating classes.
 
 ## 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]
+```python
+# Improved usage example with ordering and custom threshold
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+import numpy as np
+
+def predict_emotions(text, model_name, threshold=0.35):
+    # Load model and tokenizer
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    
+    # Tokenize and predict
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=250)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probabilities = torch.sigmoid(logits).numpy()[0]
+    
+    # Map probabilities to emotions
+    emotions = {{emotion: float(prob) for emotion, prob in zip(model.config.id2label.values(), probabilities)}}
+    
+    # Get emotions above threshold and sort by probability
+    predicted_emotions = [(emotion, prob) for emotion, prob in emotions.items() if prob >= threshold]
+    predicted_emotions.sort(key=lambda x: x[1], reverse=True)
+    
+    return {{
+        "text": {{text}},
+        "predicted_emotions": {{predicted_emotions}},
+        "all_probabilities": {{dict(sorted(emotions.items(), key=lambda x: x[1], reverse=True))}},
+        "threshold_used": {{threshold}}
+    }}
+
+# Example usage
+result = predict_emotions(
+    "I'm feeling really excited and happy about this news!", 
+    "model-name",
+    threshold=0.35  # Customize threshold here
+)
+
+# Print results
+print(f"Text: {{result['text']}}")
+print("\nDetected emotions (sorted by probability):")
+for emotion, prob in result['predicted_emotions']:
+    print(f"  - {{emotion.upper()}} ({{prob:.4f}})")
+
+print("\nAll emotion probabilities (sorted):")
+for emotion, prob in result['all_probabilities'].items():
+    print(f"  {{'*' if prob >= result['threshold_used'] else ' '}} {{emotion}}: {{prob:.4f}}")
+```
 
 
 #### 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]
-
-<!-- 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]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+### Model Architecture and Objective