Update README.md

README.md

@@ -1,199 +1,126 @@
 ---
-library_name: transformers
-tags: []
+language:
+- en
+pipeline_tag: text-classification
+license: apache-2.0
 ---
 
-# Model Card for Model ID
+# Llama-3.1-Bespoke-MiniCheck-7B
 
-<!-- Provide a quick summary of what the model is/does. -->
+This is a fact-checking model developed by [Bespoke Labs](https://bespokelabs.ai) and maintained by [Liyan Tang](https://www.tangliyan.com/) and Bespoke Labs. The model is an improvement of the MiniCheck model proposed in the following paper:
 
+📃 [**MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents**](https://arxiv.org/pdf/2404.10774.pdf)
 
+[GitHub Repo](https://github.com/Liyan06/MiniCheck)
 
-## Model Details
+The model takes as input a document and a sentence and determines whether the sentence is supported by the document: **MiniCheck-Model(document, claim) -> {0, 1}**
 
-### Model Description
+In order to fact-check a multi-sentence claim, the claim should first be broken up into sentences. The document does not need to be chunked unless it exceeds `32K` tokens.
 
-<!-- Provide a longer summary of what this model is. -->
+`Llama-3.1-Bespoke-MiniCheck-7B` is finetuned from `internlm/internlm2_5-7b-chat` ([Cai et al., 2024](https://arxiv.org/pdf/2403.17297))
+on the combination of 35K data points only:
+- 21K ANLI examples ([Nie et al., 2020](https://aclanthology.org/2020.acl-main.441.pdf))
+- 14K synthetically-generated examples following the scheme in the MiniCheck paper, but with additional proprietary data curation techniques (sampling, selecting additional high quality data sources, etc.) from Bespoke Labs. Specifically, we generate 7K "claim-to-document" (C2D) and 7K "doc-to-claim" (D2C) examples. The following steps were taken to avoid benchmark contamination: the error types of the model in the benchmark data were not used, and the data sources were curated independent of the benchmark.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+All synthetic data is generated by [`meta-llama/Meta-Llama-3.1-405B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct), thus the name `Llama-3.1-Bespoke-MiniCheck-7B`.
 
-- **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]
+**While scaling up the model (compared to what is in MiniCheck) helped, many improvements come from high-quality curation, thus establishing the superiority of Bespoke Labs's curation technology.**
 
-### Model Sources [optional]
+### Model Variants
+We also have other three MiniCheck model variants:
+- [lytang/MiniCheck-Flan-T5-Large](https://huggingface.co/lytang/MiniCheck-Flan-T5-Large) (Model Size: 0.8B)
+- [lytang/MiniCheck-RoBERTa-Large](https://huggingface.co/lytang/MiniCheck-RoBERTa-Large) (Model Size: 0.4B)
+- [lytang/MiniCheck-DeBERTa-v3-Large](https://huggingface.co/lytang/MiniCheck-DeBERTa-v3-Large) (Model Size: 0.4B)
 
-<!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Model Performance
 
-## Uses
+Leaderboard screen shot will be here.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+<!-- <p align="center">
+    <img src="./cost-vs-bacc.png" width="360">
+</p> -->
 
-### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+The performance of these models is evaluated on our new collected benchmark (unseen by our models during training), [LLM-AggreFact](https://huggingface.co/datasets/lytang/LLM-AggreFact),
+from 11 recent human annotated datasets on fact-checking and grounding LLM generations. **Llama-3.1-Bespoke-MiniCheck-7B is the SOTA
+fact-checking model, despite a small size.**
 
-[More Information Needed]
 
-### Downstream Use [optional]
+# Model Usage
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+Please first clone our [GitHub Repo](https://github.com/Liyan06/MiniCheck) and install necessary packages from `requirements.txt`.
 
-[More Information Needed]
+### License
 
-### Out-of-Scope Use
+Free for use for non-commercial purposes. For commercial licensing, please contact company@bespokelabs.ai.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+### Throughput
 
-[More Information Needed]
+We speed up Llama-3.1-Bespoke-MiniCheck-7B inference with [vLLM](https://github.com/vllm-project/vllm). Based on our test on a single A6000 (48 VRAM), Llama-3.1-Bespoke-MiniCheck-7B with vLLM and MiniCheck-Flan-T5-Large have throughputs > 500 docs/min.
 
-## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Below is a simple use case
 
-[More Information Needed]
+```python
+from minicheck.minicheck import MiniCheck
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 
-### Recommendations
+doc = "A group of students gather in the school library to study for their upcoming final exams."
+claim_1 = "The students are preparing for an examination."
+claim_2 = "The students are on vacation."
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+# model_name can be one of:
+# ['roberta-large', 'deberta-v3-large', 'flan-t5-large', 'Bespoke-MiniCheck-7B']
+scorer = MiniCheck(model_name='Bespoke-MiniCheck-7B', cache_dir='./ckpts')
+pred_label, raw_prob, _, _ = scorer.score(docs=[doc, doc], claims=[claim_1, claim_2])
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+print(pred_label) # [1, 0]
+print(raw_prob)   # [0.9859177240606697, 0.012431238696923606]
+```
 
-## How to Get Started with the Model
+### Test on our [LLM-AggreFact](https://huggingface.co/datasets/lytang/LLM-AggreFact) Benchmark
 
-Use the code below to get started with the model.
+```python
+import pandas as pd
+from datasets import load_dataset
+from minicheck.minicheck import MiniCheck
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 
-[More Information Needed]
+# load 29K test data
+df = pd.DataFrame(load_dataset("lytang/LLM-AggreFact")['test'])
+docs = df.doc.values
+claims = df.claim.values
 
-## Training Details
+scorer = MiniCheck(model_name='Bespoke-MiniCheck-7B', cache_dir='./ckpts')
+pred_label, raw_prob, _, _ = scorer.score(docs=[doc, doc], claims=[claim_1, claim_2])  # ~ 500 docs/min, depending on hardware
+```
 
-### Training Data
+To evaluate the result on the benchmark
+```python
+from sklearn.metrics import balanced_accuracy_score
 
-<!-- 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. -->
+df['preds'] = pred_label
+result_df = pd.DataFrame(columns=['Dataset', 'BAcc'])
+for dataset in df.dataset.unique():
+    sub_df = df[df.dataset == dataset]
+    bacc = balanced_accuracy_score(sub_df.label, sub_df.preds) * 100
+    result_df.loc[len(result_df)] = [dataset, bacc]
 
-[More Information Needed]
+result_df.loc[len(result_df)] = ['Average', result_df.BAcc.mean()]
+result_df.round(1)
+```
 
-### Training Procedure
+# Citation
 
-<!-- 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]
+```
+@misc{tang2024minicheck,
+      title={MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents},
+      author={Liyan Tang and Philippe Laban and Greg Durrett},
+      year={2024},
+      eprint={2404.10774},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```