Upload README.md (#2)

- Upload README.md (329fb5625980b31ed4bafd38354c8d010535ed2f)


Co-authored-by: Minjee Yang <mind22@users.noreply.huggingface.co>

README.md

@@ -1,199 +1,157 @@
 ---
 library_name: transformers
-tags: []
+language:
+- en
+base_model:
+- google/gemma-2-2b-it
 ---
 
-# Model Card for Model ID
+# Model Card for ScriptWave Gemma-2-2b-it
 
 <!-- Provide a quick summary of what the model is/does. -->
-
+This model is designed to generate scripts based on user-provided scene descriptions and character names. It not only creates dialogues between characters but also analyzes the emotions within the generated script. After determining the emotional tone, the model recommends music that fits the identified emotions. These music suggestions make the tool useful for creative writing and content production by aligning dialogues with appropriate soundtracks.
 
 
 ## 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:** Chanjeans, mind22
+- **Model type:** Causal Language Model (AutoModelForCausalLM)
+- **Language(s) (NLP):** English
+- **Finetuned from model [optional]:** google/gemma-2-2b-it
+
+
+
+
 
-- **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]
+- **Repository:** https://github.com/minj22/scriptwave
 
-## 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. -->
+
+## Uses
 
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+- **Script Generation**: Generates dialogue scripts based on user inputs including scene description, character names, and tone or genre.
 
-[More Information Needed]
+- **Music Recommendation**: Analyzes generated scripts to recommend music tracks that align with the emotional tone of the dialogue.
 
 ### Downstream Use [optional]
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+- **Creative Writing**: Can be utilized by writers for brainstorming and drafting scripts.
 
-[More Information Needed]
+- **Content Creation**: Useful in video production or gaming for character dialogue and scene settings.
 
 ### 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 to create harmful or misleading content, including hate speech, disinformation, or any adult content.
+
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- Bias in Output: The model may reflect biases present in the training data, leading to stereotypical representations of characters or scenarios.
+- Limitations in Context Understanding: The model may struggle with understanding nuanced emotional tones or context, impacting script quality.
+- Music Recommendation Accuracy: Recommendations may not always align with user expectations, as they are based solely on emotion analysis.
 
 ### Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Users should critically evaluate the generated content and be aware of the potential biases in character representations and emotional analyses. Manual oversight is recommended for sensitive topics.
+
 
-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 AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Chanjeans/scriptgenerate_musicrecommend"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+scene_description = input("Describe the scene (e.g., A heated argument at a dinner party): ")
+character_1 = input("Enter the name of the first character: ")
+character_2 = input("Enter the name of the second character: ")
+genre_or_tone = input("Describe the genre or tone (e.g., Romantic, Thriller, Comedy): ")
+
+test_input = f"""
+INT. LOCATION - DAY
+{scene_description}
+{character_1.upper()}
+(in a {genre_or_tone.lower()} tone)
+I never thought it would come to this...
+
+{character_2.upper()}
+(reacting in a {genre_or_tone.lower()} manner)
+Well, here we are. What are you going to do about it?
+
+{character_1.upper()}
+(pausing, thinking)
+I don't know... maybe it's time I finally did something about this.
+"""
+
+input_ids = tokenizer.encode(test_input, return_tensors="pt")
+
+output = model.generate(
+    input_ids,
+    max_length=400,  
+    num_return_sequences=1,
+    pad_token_id=tokenizer.eos_token_id
+)
+
+generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+print("Generated script:\n", generated_text)
+```
 
 ## 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. -->
+https://huggingface.co/datasets/li2017dailydialog/daily_dialog
 
-[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]
+```python
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    target_modules=["gate_proj", "up_proj", "down_proj"],
+    lora_dropout=0.2,
+    bias="none",
+    task_type=TaskType.CAUSAL_LM
+)
+```
+```python
+training_args = TrainingArguments(
+    output_dir='./results',
+    per_device_train_batch_size=2,
+    num_train_epochs=1,
+    gradient_accumulation_steps=16,
+    fp16=True,
+    logging_steps=100,
+    save_steps=500,
+    save_total_limit=2,
+    learning_rate=5e-5,
+    warmup_steps=500,
+    lr_scheduler_type="linear"
+)
+```
 
 #### 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]
+The model demonstrates capability in generating contextually relevant scripts and making music recommendations based on emotional analysis, making it a valuable tool for creative writers and content creators.