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README.md

@@ -1,73 +1,202 @@
-# LS-LLaMA: Label Supervised LLaMA Finetuning
+---
+base_model: NousResearch/Llama-2-7b-hf
+library_name: peft
+---
 
-<h2>📢: For convenience, we build a bi-directional LLMs toolkit <a href='https://github.com/WhereIsAI/BiLLM'>BiLLM</a> for language understanding. Welcome to use it.</h2>
+# Model Card for Model ID
 
-<p align="center">
+<!-- Provide a quick summary of what the model is/does. -->
 
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/label-supervised-llama-finetuning/named-entity-recognition-on-conll03-4)](https://paperswithcode.com/sota/named-entity-recognition-on-conll03-4?p=label-supervised-llama-finetuning)
 
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/label-supervised-llama-finetuning/named-entity-recognition-on-ontonotes-5-0-1)](https://paperswithcode.com/sota/named-entity-recognition-on-ontonotes-5-0-1?p=label-supervised-llama-finetuning)
-</p>
 
+## Model Details
 
-<p align='center'>
-<img src='./docs/lsllama.png'/>
-</p>
+### Model Description
 
-## Usage
+<!-- Provide a longer summary of what this model is. -->
 
-Our implementation currently supports the following sequence classification benchmarks: 
-1. SST2 (2 classes) / SST5 (5 classes)
-2. AGNews (4 classes)
-3. Twitter Financial News Sentiment (twitterfin, 3 classes)
 
-and token classification benchmarks for named entity recognition (NER): CoNLL2003 and OntonotesV5.
 
-Commands for training LS-LLaMA and LS-unLLaMA on different tasks can follow the templates below:
-```console
-foo@bar:~$ CUDA_VISIBLE_DEVICES=0 python file_name.py dataset_name model_size
-```
+- **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]
 
-`file_name.py` can be one of `unllama_seq_clf.py`, `unllama_token_clf.py`, `llama_seq_clf.py`, and `llama_token_clf.py`, for training LS-LLaMA and LS-unLLaMA on sequence- and token-level classification.
+### Model Sources [optional]
 
-`dataset_name` can be one of `sst2`, `sst5`, `agnews`, `twitterfin`, `conll03`, and `ontonotesv5`.
+<!-- Provide the basic links for the model. -->
 
-`model_size` can be `7b` or `13b`, corresponding to LLaMA-2-7B and LLaMA-2-13B.
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-For example, the following command will train LS-unLLaMA based on LLaMA-2-7B on AGNews for sequence classification:
-```console
-foo@bar:~$ CUDA_VISIBLE_DEVICES=0 python unllama_seq_clf.py agnews 7b
-```
+## Uses
 
-## Implementations
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-Load Pretrained Models
+### Direct Use
 
-```python
-from transformers import AutoTokenizer
-from modeling_llama import (
-    LlamaForSequenceClassification, LlamaForTokenClassification,
-    UnmaskingLlamaForSequenceClassification, UnmaskingLlamaForTokenClassification,
-)
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
+[More Information Needed]
 
-model_id = 'meta-llama/Llama-2-7b'
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = LlamaForSequenceClassification.from_pretrained(model_id).bfloat16()
-model = LlamaForTokenClassification.from_pretrained(model_id).bfloat16()
-model = UnmaskingLlamaForSequenceClassification.from_pretrained(model_id).bfloat16()
-model = UnmaskingLlamaForTokenClassification.from_pretrained(model_id).bfloat16()
-```
+### Downstream Use [optional]
 
-For more usage, please refer to `unllama_seq_clf.py`, `unllama_token_clf.py`, `llama_seq_clf.py`, `llama_token_clf.py`.
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-# Citation
+[More Information Needed]
 
-```
-@article{li2023label,
-  title={Label supervised llama finetuning},
-  author={Li, Zongxi and Li, Xianming and Liu, Yuzhang and Xie, Haoran and Li, Jing and Wang, Fu-lee and Li, Qing and Zhong, Xiaoqin},
-  journal={arXiv preprint arXiv:2310.01208},
-  year={2023}
-}
-```
+### 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. -->
+
+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]
+
+## 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]
+
+
+#### 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]
+### Framework versions
+
+- PEFT 0.12.0