Update README.md

README.md

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-#Factual Consistency Evaluator/Metric in ACL 2023 paper
+# Factual Consistency Evaluator/Metric in ACL 2023 paper
 
 *[WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning
 ](https://arxiv.org/abs/2212.10057)*
 
-#Introduction
-WeCheck
+## Model description
+WeCheck is a factual consistency metric trained from weakly annotated samples.
 
+This WeCheck checkpoint can be used to check the following three generation tasks:
+
+**Text Summarization/Knowlege grounded dialogue Generation/Paraphrase**
+
+This WeCheck checkpoint is trained based on the following three weak labler:
+
+*[QAFactEval
+](https://github.com/salesforce/QAFactEval)* / *[Summarc](https://github.com/tingofurro/summac)* / *[NLI warmup](https://huggingface.co/MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli)* 
 ---
+
+### How to use the model
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+model_name = "nightdessert
+/
+WeCheck "
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing."
+hypothesis = "The movie was not good."
+input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
+output = model(input["input_ids"].to(device))  # device = "cuda:0" or "cpu"
+prediction = torch.softmax(output["logits"][0], -1).tolist()
+label_names = ["entailment", "neutral", "contradiction"]
+prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
+print(prediction)
+
 license: openrail
 pipeline_tag: text-classification
 tags: