Create README.md

README.md

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+---
+language:
+- en
+pipeline_tag: text-classification
+---
+# Model Summary
+
+This is a fact-checking model from our work:
+
+📃 [**MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents**](https://arxiv.org/pdf/2404.10774.pdf) ([GitHub Repo](https://github.com/Liyan06/MiniCheck))
+
+The model is based on RoBERTA-Large that predicts a binary label - 1 for supported and 0 for unsupported. 
+The model is doing predictions on the *sentence-level*. It takes as input a document and a sentence and determine 
+whether the sentence is supported by the document: **MiniCheck-Model(document, claim) -> {0, 1}**
+
+
+MiniCheck-RoBERTa-Large is fine tuned from the trained RoBERTA-Large model from AlignScore ([Zha et al., 2023](https://aclanthology.org/2023.acl-long.634.pdf)) 
+on 14K synthetic data generated from scratch in a structed way (more details in the paper).
+
+
+### Model Variants
+We also have other two MiniCheck model variants:
+- [lytang/MiniCheck-Flan-T5-Large](https://huggingface.co/lytang/MiniCheck-Flan-T5-Large)
+- [lytang/MiniCheck-DeBERTa-v3-Large](https://huggingface.co/lytang/MiniCheck-DeBERTa-v3-Large)
+
+
+### Model Performance
+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 10 recent human annotated datasets on fact-checking and grounding LLM generations. MiniCheck-RoBERTa-Large outperform all
+exisiting specialized fact-checkers with a similar scale by a large margin but is 2% worse than our best model MiniCheck-Flan-T5-Large. See full results in our work.
+
+Note: We only evaluated the performance of our models on real claims -- without any human intervention in 
+any format, such as injecting certain error types into model-generated claims. Those edited claims do not reflect
+LLMs' actual behaviors.
+
+
+# Model Usage Demo
+
+Please first clone our [GitHub Repo](https://github.com/Liyan06/MiniCheck) and install necessary packages from `requirements.txt`.
+
+
+### Below is a simple use case
+
+```python
+from minicheck.minicheck import MiniCheck
+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."
+
+# model_name can be one of ['roberta-large', 'deberta-v3-large', 'flan-t5-large']
+scorer = MiniCheck(model_name='roberta-large', device=f'cuda:0', cache_dir='./ckpts')
+pred_label, raw_prob, _, _ = scorer.score(docs=[doc, doc], claims=[claim_1, claim_2])
+print(pred_label) # [1, 0]
+print(raw_prob)   # [0.9581979513168335, 0.031335990875959396]
+```
+
+### Test on our [LLM-AggreFact](https://huggingface.co/datasets/lytang/LLM-AggreFact) Benchmark
+
+```python
+import pandas as pd
+from datasets import load_dataset
+from minicheck.minicheck import MiniCheck
+
+# load 13K test data
+df = pd.DataFrame(load_dataset("lytang/LLM-AggreFact")['test'])
+docs = df.doc.values
+claims = df.claim.values
+
+scorer = MiniCheck(model_name='roberta-large', device=f'cuda:0', cache_dir='./ckpts')
+pred_label, raw_prob, _, _ = scorer.score(docs=docs, claims=claims)  # ~ 15 mins, depending on hardware
+```
+
+To evalaute the result on the benchmark
+```python
+from sklearn.metrics import balanced_accuracy_score
+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]
+result_df.loc[len(result_df)] = ['Average', result_df.BAcc.mean()]
+result_df.round(1)
+```
+
+# Citation
+
+```
+@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}
+}
+```