Update README.md

README.md

@@ -1,199 +1,105 @@
----
-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for crpatel/mistral_prompt_tuning_tweet_classifier
 
 ## Model Details
 
 ### Model Description
+This model is a fine-tuned version of Mistral, specifically trained for classifying tweets as complaints or non-complaints. The fine-tuning has been done using PEFT (Parameter-Efficient Fine-Tuning) and prompt tuning techniques. The model leverages the "ought/raft" dataset, specifically the "twitter_complaints" subset.
 
-<!-- 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:** crpatel
+- **Shared by [optional]:** crpatel
+- **Model type:** Causal Language Model (Fine-tuned with PEFT)
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** Mistral
+- **Base Model Credit:** The base model, Mistral, was developed by [Mistral AI](https://mistral.ai/).
 
-- **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]
+### Model Sources
+- **Repository:** [Hugging Face Model Hub](https://huggingface.co/crpatel/mistral_prompt_tuning_tweet_classifier)
+- **Dataset:** [RAFT - Twitter Complaints](https://huggingface.co/datasets/ought/raft)
+- **Base Model:** [Mistral AI](https://mistral.ai/)
 
 ## 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 model can be used for classifying tweets as complaints or non-complaints, which can be useful for customer service automation, sentiment analysis, and social media monitoring.
 
-<!-- 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]
+### Downstream Use
+The model can be fine-tuned further for other social media classification tasks or sentiment analysis applications in customer support systems.
 
 ### 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 not designed for general sentiment analysis outside of complaint detection. Misuse for legal or high-stakes decision-making without validation is discouraged.
 
 ## 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. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- The model should be evaluated for biases before deployment.
+- Users should verify results against human-labeled datasets.
+- The model may not generalize well to tweets outside the training distribution.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from datasets import load_dataset
+import torch
 
-[More Information Needed]
+dataset = load_dataset("ought/raft", "twitter_complaints")
+peft_model_id = "crpatel/mistral_prompt_tuning_tweet_classifier"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+text_column = "Tweet text"
+label_column = "text_label"
+
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, torch_dtype=torch.float16).to(device)
+model = PeftModel.from_pretrained(model, peft_model_id)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+```
 
 ## 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]
+- **Dataset:** Ought/RAFT - Twitter Complaints
+- **Preprocessing:** Tokenization with AutoTokenizer
+- **Filtering:** Preprocessing steps included lowercasing and cleaning tweet text
 
 ### 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]
+- **Optimization Algorithm:** AdamW
+- **Precision:** FP16 for memory efficiency
+- **Batch Size:** 16
+- **Learning Rate:** 5e-5
 
 ### Model Architecture and Objective
+The model is based on Mistral and fine-tuned using PEFT to optimize efficiency in tweet classification.
 
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
 
 #### Software
+- **Transformers Library:** 🤗 Transformers
+- **Training Framework:** PyTorch
+- **Fine-Tuning:** PEFT (Parameter-Efficient Fine-Tuning)
+
+## Citation
+If you use this model, please cite:
+
+```bibtex
+@article{crpatel2024,
+  author = {C.R. Patel},
+  title = {Mistral Prompt Tuning Tweet Classifier},
+  year = {2024},
+  publisher = {Hugging Face},
+  journal = {Hugging Face Model Hub}
+}
+```
+
+Additionally, credit the base model:
+
+```bibtex
+@article{mistral2023,
+  author = {Mistral AI},
+  title = {Mistral Language Model},
+  year = {2023},
+  publisher = {Mistral AI},
+  journal = {Mistral AI Model Hub}
+}
+```
 
-[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]