Update README_EN.md

README_EN.md

@@ -178,8 +178,11 @@ Here [schema](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC
 
 # 5.Convert script
 
-A script named [convert.py](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/kg2instruction/convert.py) is provided to facilitate the uniform conversion of data into KnowLM instructions. The [data](https://github.com/zjunlp/DeepKE/tree/main/example/llm/InstructKGC/data) directory contains the expected data format for each task before executing convert.py.
+**Training Data Transformation**
 
+Before inputting data into the model, it needs to be formatted to include `instruction` and `input` fields. To assist with this, we offer a script [kg2instruction/convert.py](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/kg2instruction/convert.py), which can batch convert data into a format directly usable by the model.
+
+> Before using the [kg2instruction/convert.py](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/kg2instruction/convert.py) script, please ensure you have referred to the [data](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/data) directory. Please consult `sample.json` to understand the format of the data before conversion, `schema.json` illustrates the organization of the schema, and `processed.json` describes the format of the data after conversion.
 
 
 ```bash              
@@ -196,8 +199,11 @@ python kg2instruction/convert.py \
 ```
 
 
+**Negative Sampling**: Assuming dataset A contains labels [a, b, c, d, e, f], for a given sample s, it might involve only labels a and b. Our objective is to randomly introduce some relationships from the candidate relationship list that were originally unrelated to s, such as c and d. However, it's worth noting that in the output, the labels for c and d either won't be included, or they will be output as `NAN`.
+
 The `schema_path` specifies the path to a schema file (a JSON file). The schema file consists of three lines of JSON strings, organized in a fixed format. Taking Named Entity Recognition (NER) as an example, the meanings of each line are as follows:
 
+
 ```
 ["BookTitle", "Address", "Movie", ...]  # List of entity types
 []  # Empty list
@@ -238,8 +244,13 @@ For Event Extraction with Arguments (EEA) tasks:
 </details>
 
 
+For more detailed information on the schema file, you can refer to the `schema.json` file in the respective task directories under the [data](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/data) directory.
+
+
+**Testing Data Transformation**
+
+For test data, you can use the [kg2instruction/convert_test.py](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/kg2instruction/convert_test.py) script, which does not require the data to contain label fields (`entity`, `relation`, `event`), just the input field and the corresponding schema_path.
 
-[convert_test.py](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/kg2instruction/convert_test.py) does not require data to have label (`entity`, `relation`, `event`) fields, only needs to have an `input` field and provide a `schema_path` is suitable for processing test data.
 
 ```bash
 python kg2instruction/convert_test.py \
@@ -251,6 +262,8 @@ python kg2instruction/convert_test.py \
     --sample 0 
 ```
 
+**Data Transformation Examples**
+
 
 Here is an example of data conversion for Named Entity Recognition (NER) task: