Update README.md

README.md

@@ -26,17 +26,17 @@ This fine-tuned BERT model is a multilabel multiclass classifier designed to pre
 
 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:** [Sinanmz]
-- **Model type:** [Multiclass Multilabel Classifier]
-- **Language(s) (NLP):** [English]
-- **License:** [MIT]
-- **Finetuned from model [optional]:** [bert-base-uncased]
+- **Developed by:** Sinanmz
+- **Model type:** Multiclass Multilabel Classifier
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model optional:** google-bert/bert-base-uncased
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [https://github.com/Sinanmz/MIR]
+- **Repository:** https://github.com/Sinanmz/MIR
 
 ## Uses
 
@@ -62,69 +62,51 @@ This BERT-based multilabel multiclass classifier is designed to predict the genr
 - **Content Creators:** Gaining better visibility through accurate classification and tagging of their work.
 - **Platform Operators:** Improving user engagement and satisfaction with more personalized and accurate content delivery.
 
+## How to Get Started with the Model
 
-### Direct 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]
-
-## Bias, Risks, and Limitations
-
-<!-- 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. -->
+Here's the "How to Get Started with the Model" section for your model card:
 
-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.
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-## Training Details
+# Load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained('Sinanmz/Movie_Genre_Classifier')
+model = AutoModelForSequenceClassification.from_pretrained('Sinanmz/Movie_Genre_Classifier')
 
-### Training Data
+# Example movie summary (summary of Dune: Part Two)
+movie_summary = """Paul Atreides unites with Chani and the Fremen while on a warpath of 
+revenge against the conspirators who destroyed his family. Facing a choice between the 
+love of his life and the fate of the known universe, he endeavors to prevent a terrible 
+future only he can foresee."""
 
-<!-- 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. -->
+# Tokenize the input
+inputs = tokenizer(movie_summary, return_tensors="pt", truncation=True, padding=True)
 
-[More Information Needed]
+# Get model predictions
+outputs = model(**inputs)
+logits = outputs.logits
 
-### Training Procedure
+# Convert logits to probabilities
+probs = torch.sigmoid(logits)
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+# Print the predicted genres
+genre_labels = ["Action", "Drama", "Comedy", "Animation", "Crime"]
+predicted_genres = [genre_labels[i] for i in range(len(genre_labels)) if probs[0][i] >= 0.5]
 
-#### Preprocessing [optional]
+print(f"Predicted genres: {predicted_genres}")
 
-[More Information Needed]
-
-
-#### Training Hyperparameters
+# Output:
+# Predicted genres: ['Action', 'Drama']
+```
 
-- **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