Improve Model Card for SHARE LoRA Adapter

README.md

@@ -1,199 +1,149 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+pipeline_tag: feature-extraction
+language: en
+datasets:
+  - SHARE
+base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
+tags:
+  - dialogue
+  - long-term-dialogue
+  - memory
+  - conversational
+  - llama
+  - llm-adapter
+  - peft
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# SHARE: Shared Memory-Aware Open-Domain Long-Term Dialogue Model
 
+This model is a LoRA adapter for `meta-llama/Meta-Llama-3.1-8B-Instruct`, developed as part of the research presented in the paper [SHARE: Shared Memory-Aware Open-Domain Long-Term Dialogue Dataset Constructed from Movie Script](https://huggingface.co/papers/2410.20682).
 
+The paper introduces **SHARE**, a new long-term dialogue dataset constructed from movie scripts to leverage shared memories for more engaging conversations. It also presents **EPISODE**, a long-term dialogue framework that utilizes these shared experiences. This model facilitates the extraction of relevant shared memory features from dialogue for downstream tasks related to long-term dialogue understanding and generation, playing a crucial role in enabling the EPISODE framework.
 
 ## Model Details
 
 ### Model Description
+This model is a PEFT (LoRA) adapter fine-tuned on [`meta-llama/Meta-Llama-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct). It is designed to work with the SHARE dataset and the EPISODE framework for leveraging shared memories in long-term dialogue. Its primary function is to enable the extraction of contextual features that capture "shared memories" for enhancing conversational agents and enabling more engaging and sustainable long-term dialogues.
 
-<!-- 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 type:** LoRA adapter for Causal Language Model (Llama)
+-   **Language(s) (NLP):** English
+-   **License:** MIT
+-   **Finetuned from model:** `meta-llama/Meta-Llama-3.1-8B-Instruct`
 
 ### 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:** [More Information Needed]
+-   **Paper:** [SHARE: Shared Memory-Aware Open-Domain Long-Term Dialogue Dataset Constructed from Movie Script](https://huggingface.co/papers/2410.20682)
+-   **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. -->
-
 ### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+This model, when loaded with its base LLM, can be used to extract features that represent shared memories and contextual information from dialogue inputs. These features can then be utilized in various long-term dialogue applications to enhance conversational engagement and coherence within the EPISODE framework.
 
 ### 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]
+Potential downstream uses include building more engaging and context-aware conversational AI systems that can maintain long-term context and leverage historical interactions, improving chatbots, and advancing research in dialogue systems that require persistent memory and context.
 
 ### 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 specifically trained for long-term dialogue related to shared memories and may not perform optimally for general-purpose text generation or other NLP tasks unrelated to its fine-tuning domain. It should not be used for generating harmful, biased, or misleading content without careful evaluation.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model's performance and potential biases are inherently tied to its training data, the SHARE dataset, which is constructed from movie scripts. As such, it may inherit biases present in cinematic narratives, such as stereotypes, oversimplifications of human relationships, or a focus on specific types of interactions. Further evaluation on diverse datasets is recommended to identify and mitigate any such limitations.
 
 ### 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 (both direct and downstream) should be made aware of the model's training data source and its potential for reflecting biases present in movie scripts. Careful ethical review and bias mitigation strategies should be applied before deploying in real-world scenarios, especially in sensitive applications.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+To use this LoRA adapter, you need to load the base model (`meta-llama/Meta-Llama-3.1-8B-Instruct`) and then load the LoRA adapter on top of it using the `peft` library.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+import torch
+
+# Define your specific model ID on the Hugging Face Hub
+# Replace 'your_org/your_model_name' with the actual model ID (e.g., 'naver-api-ai/SHARE_v1')
+peft_model_id = "your_org/your_model_name" 
+
+# Load the base model
+base_model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+# Ensure to specify torch_dtype and device_map for efficient loading if available
+base_model = AutoModelForCausalLM.from_pretrained(base_model_id, torch_dtype=torch.bfloat16, device_map="auto")
+
+# Load the LoRA adapter on top of the base model
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+# Optional: merge LoRA weights into the base model for easier use, if you don't plan further training
+# model = model.merge_and_unload() 
+
+# Example usage for dialogue processing or feature extraction (conceptual)
+# The exact method to extract "features" (e.g., embeddings of specific tokens
+# representing shared memories) would depend on the implementation of the SHARE framework
+# and the EPISODE framework as described in the paper.
+
+# For basic text generation (after merging LoRA, or if using PeftModel directly with .generate()):
+# prompt = "Character A: Hi, do you remember our trip to the old lighthouse? Character B: Oh, yes! That stormy day..."
+# inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+# outputs = model.generate(**inputs, max_new_tokens=100)
+# generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+# print(generated_text)
+
+# For accessing hidden states for feature extraction (conceptual):
+# inputs = tokenizer("A dialogue turn that might contain shared memory cues.", return_tensors="pt")
+# outputs = model(**inputs, output_hidden_states=True)
+# # The features for "shared memories" would likely be specific representations
+# # from certain layers or derived from attention mechanisms, as per the paper.
+# all_hidden_states = outputs.hidden_states
+# # Further processing would be needed to extract relevant "shared memory" features.
+
+print("Model loaded and ready for use with the base model!")
+```
 
 ## 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]
+The model was fine-tuned using the **SHARE dataset**, which is a novel long-term dialogue dataset constructed from movie scripts. This dataset is designed to be rich in explicit persona information, event summaries, and implicitly extractable shared memories between conversational participants.
 
 ### 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]
+This model uses LoRA (Low-Rank Adaptation) for fine-tuning. Specific training hyperparameters and the training regime would typically be detailed in the associated paper or the project's official code repository.
 
 ## 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]
+The paper describes experiments conducted using the SHARE dataset to demonstrate that shared memories make long-term dialogues "more engaging and sustainable."
 
 #### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+The evaluation focuses on the impact of shared memories on dialogue engagement and sustainability.
 
 #### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+The effectiveness of the EPISODE framework and the role of shared memories are measured by their ability to make dialogues more engaging and sustainable, and by how effectively shared memories are managed during conversation.
 
 ### Results
+The paper demonstrates that shared memories between two individuals make long-term dialogues more engaging and sustainable, and that the EPISODE framework effectively manages shared memories during dialogue.
 
-[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]
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-#### 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]
+## Citation
 
-## Model Card Contact
+If you find the SHARE dataset, the EPISODE framework, or this model helpful in your research, please consider citing the original paper:
 
-[More Information Needed]
+```bibtex
+@article{share2024longterm,
+  title={SHARE: Shared Memory-Aware Open-Domain Long-Term Dialogue Dataset Constructed from Movie Script},
+  author={Anonymous}, % Authors not provided in the prompt
+  journal={arXiv preprint arXiv:2410.20682},
+  year={2024},
+  url={https://arxiv.org/abs/2410.20682}
+}
+```