update

README.md

@@ -7,23 +7,8 @@ Compared to zjunlp/knowlm-13b-zhixi, zjunlp/knowlm-13b-ie exhibits slightly stro
 zjunlp/knowlm-13b-ie samples around 10% of the data from Chinese-English information extraction datasets, which then undergo negative sampling. For instance, if dataset A contains labels [a, b, c, d, e, f], we first sample 10% of the data from A. For a given sample 's', it might only contain labels a and b. We randomly add relationships that it doesn't originally have, such as c and d, from the specified list of relation candidates. When encountering these additional relationships, the model might output text similar to 'NAN'.This method equips the model with the ability to generate 'NAN' outputs to a certain extent, enhancing its information extraction capability while weakening its generalization ability.
 
 
-# 2.Usage
-We provide a script, [inference.py](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/src/inference.py), for direct inference using the `zjunlp/knowlm-13b-ie model``. Please refer to the README.md at https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/ for environment configuration and other details.
 
-```bash
-CUDA_VISIBLE_DEVICES="0" python src/inference.py \
-    --model_name_or_path 'models/knowlm-13b-ie' \
-    --model_name 'llama' \
-    --input_file 'data/NER/processed.json' \
-    --output_file 'results/ner_test.json' \
-    --fp16 
-```
-
-If GPU memory is not enough, you can use `--bits 8` or `--bits 4`.
-
-
-
-# 3.IE template
+# 2.IE template
 NER supports the following templates:
 ```python
 entity_template_zh =  {
@@ -55,7 +40,10 @@ Both the schema and format placeholders ({s_schema} and {s_format}) are embedded
 
 For a more comprehensive understanding of the templates, please refer to the files [ner_template.py](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/kg2instruction/ner_template.py)、[re_template.py](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/kg2instruction/re_template.py)、[ee_template.py](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/kg2instruction/ee_template.py) .
 
-# 4.Convert script
+
+
+
+# 3.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.
 
@@ -71,6 +59,24 @@ python kg2instruction/convert.py \
   --all
 ```
 
+
+
+# 4.Usage
+We provide a script, [inference.py](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/src/inference.py), for direct inference using the `zjunlp/knowlm-13b-ie model`. Please refer to the [README.md](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/README.md) for environment configuration and other details.
+
+```bash
+CUDA_VISIBLE_DEVICES="0" python src/inference.py \
+    --model_name_or_path 'models/knowlm-13b-ie' \
+    --model_name 'llama' \
+    --input_file 'data/NER/processed.json' \
+    --output_file 'results/ner_test.json' \
+    --fp16 
+```
+
+If GPU memory is not enough, you can use `--bits 8` or `--bits 4`.
+
+
+
 # 5.Evaluate
 
 We provide a script at [evaluate.py](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/kg2instruction/evaluate.py) to convert the string output of the model into a list and calculate F1

README_CN.md

@@ -5,23 +5,8 @@
 zjunlp/knowlm-13b-ie 从中英文信息抽取数据集中采样约 10% 的数据，然后进行负采样。例如，如果数据集 A 包含标签 [a，b，c，d，e，f]，我们首先从 A 中采样出 10% 的数据。对于给定的样本 s，它可能只包含标签 a 和 b。我们随机地添加原本没有的关系，比如来自指定关系候选列表的 c 和 d。当遇到这些额外的关系时，模型可能会输出类似 'NAN' 的文本。这种方法使模型在一定程度上具备生成 'NAN' 输出的能力，增强了其信息抽取能力，但削弱了其泛化能力。
 
 
-# 2. 使用
-我们提供了可直接使用 `zjunlp/knowlm-13b-ie` 模型进行推理的脚本[inference.py](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/src/inference.py), 请参考 https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/README.md 配置环境等。
 
-```bash
-CUDA_VISIBLE_DEVICES="0" python src/inference.py \
-    --model_name_or_path 'models/knowlm-13b-ie' \
-    --model_name 'llama' \
-    --input_file 'data/NER/processed.json' \
-    --output_file 'results/ner_test.json' \
-    --fp16 
-```
-
-如果GPU显存不足够, 可以采用 `--bits 8` 或 `--bits 4`
-
-
-
-# 3. 信息抽取模板
+# 2. 信息抽取模板
 命名实体识别（NER）支持以下模板：
 
 ```python
@@ -56,7 +41,7 @@ entity_int_out_format_en = {
 
 
 
-# 4. 转换脚本
+# 3. 转换脚本
 
 提供一个名为 [convert.py](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/kg2instruction/convert.py) 的脚本，用于将数据统一转换为可以直接输入 KnowLM 的指令。在执行 convert.py 之前，请参考 [data](https://github.com/zjunlp/DeepKE/tree/main/example/llm/InstructKGC/data) 目录中包含了每个任务的预期数据格式。
 
@@ -71,6 +56,22 @@ python kg2instruction/convert.py \
   --all
 ```
 
+
+# 4. 使用
+我们提供了可直接使用 `zjunlp/knowlm-13b-ie` 模型进行推理的脚本[inference.py](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/src/inference.py), 请参考 [README.md](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/README.md) 配置环境等。
+
+```bash
+CUDA_VISIBLE_DEVICES="0" python src/inference.py \
+    --model_name_or_path 'models/knowlm-13b-ie' \
+    --model_name 'llama' \
+    --input_file 'data/NER/processed.json' \
+    --output_file 'results/ner_test.json' \
+    --fp16 
+```
+
+如果GPU显存不足够, 可以采用 `--bits 8` 或 `--bits 4`
+
+
 # 5. 评估
 我们提供一个位于 [evaluate.py](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/kg2instruction/evaluate.py) 的脚本，用于将模型的字符串输出转换为列表并计算 F1 分数。