Update README.md

README.md

@@ -17,7 +17,7 @@ The model is an improvement of the MiniCheck model proposed in the following pap
 
 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}**
 
-**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.
+**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. Depending on use cases, adjusting chunk size may yield better performance.
 
 `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:
@@ -68,7 +68,7 @@ claim_2 = "The students are on vacation."
 # model_name can be one of:
 # ['roberta-large', 'deberta-v3-large', 'flan-t5-large', 'Bespoke-MiniCheck-7B']
 scorer = MiniCheck(model_name='Bespoke-MiniCheck-7B', enable_prefix_caching=False, cache_dir='./ckpts')
-pred_label, raw_prob, _, _ = scorer.score(docs=[doc, doc], claims=[claim_1, claim_2])
+pred_label, raw_prob, _, _ = scorer.score(docs=[doc, doc], claims=[claim_1, claim_2]) # can set `chunk_size=your-specified-value` here, default to 32K chunk size. 
 
 print(pred_label) # [1, 0]
 print(raw_prob)   # [0.9840446675150499, 0.010986349594852094]