Update README.md

README.md

@@ -1,138 +1,140 @@
----
-language: 
-- en
-tags:
-- code
-- autocomplete
-- pytorch
-- en
-license: "apache-2.0"
----
-
-# GPT2 for Code AutoComplete Model
-code-autocomplete, a code completion plugin for Python.
-
-**code-autocomplete** can automatically complete the code of lines and blocks with GPT2.
-
-## Usage
-
-Open source repo：[code-autocomplete](https://github.com/shibing624/code-autocomplete)，support GPT2 model, usage：
-
-```python
-from autocomplete.gpt2_coder import GPT2Coder
-
-m = GPT2Coder("shibing624/code-autocomplete-distilgpt2-python")
-print(m.generate('import torch.nn as')[0])
-```
-
-Also, use huggingface/transformers：
-
-*Please use 'GPT2' related functions to load this model!*
-
-```python
-import os
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-
-os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
-
-tokenizer = GPT2Tokenizer.from_pretrained("shibing624/code-autocomplete-distilgpt2-python")
-model = GPT2LMHeadModel.from_pretrained("shibing624/code-autocomplete-distilgpt2-python")
-
-prompts = [
-    """from torch import nn
-    class LSTM(Module):
-        def __init__(self, *,
-                     n_tokens: int,
-                     embedding_size: int,
-                     hidden_size: int,
-                     n_layers: int):""",
-    """import numpy as np
-    import torch
-    import torch.nn as""",
-    "import java.util.ArrayList",
-    "def factorial(n):",
-]
-for prompt in prompts:
-    input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors='pt')
-    outputs = model.generate(input_ids=input_ids,
-                             max_length=64 + len(prompt),
-                             temperature=1.0,
-                             top_k=50,
-                             top_p=0.95,
-                             repetition_penalty=1.0,
-                             do_sample=True,
-                             num_return_sequences=1,
-                             length_penalty=2.0,
-                             early_stopping=True)
-    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    print(decoded)
-    print("=" * 20)
-```
-
-output:
-```shell
-from torch import nn
-    class LSTM(Module):
-        def __init__(self, *,
-                     n_tokens: int,
-                     embedding_size: int,
-                     hidden_size: int,
-                     n_layers: int):
-            self.embedding_size = embedding_size
-====================
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-```
-
-Model files：
-```
-code-autocomplete-distilgpt2-python
-├── config.json
-├── merges.txt
-├── pytorch_model.bin
-├── special_tokens_map.json
-├── tokenizer_config.json
-└── vocab.json
-```
-
-### Train data
-#### pytorch_awesome projects source code
-
-download [code-autocomplete](https://github.com/shibing624/code-autocomplete),
-```shell
-cd autocomplete
-python create_dataset.py
-```
-
-If you want train code-autocomplete GPT2 model，refer [https://github.com/shibing624/code-autocomplete/blob/main/autocomplete/gpt2_coder.py](https://github.com/shibing624/code-autocomplete/blob/main/autocomplete/gpt2_coder.py)
-
-
-### About GPT2
-
-Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
-
-Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
-[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
-and first released at [this page](https://openai.com/blog/better-language-models/).
-
-Disclaimer: The team releasing GPT-2 also wrote a
-[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
-has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
-
-
-## Citation
-
-```latex
-@misc{code-autocomplete,
-  author = {Xu Ming},
-  title = {code-autocomplete: Code AutoComplete with GPT model},
-  year = {2022},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  url = {https://github.com/shibing624/code-autocomplete},
-}
-```
-
-
+---
+language:
+- en
+tags:
+- code
+- autocomplete
+- pytorch
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+widget:
+  - text: import torch.nn as
+---
+
+# GPT2 for Code AutoComplete Model
+code-autocomplete, a code completion plugin for Python.
+
+**code-autocomplete** can automatically complete the code of lines and blocks with GPT2.
+
+## Usage
+
+Open source repo：[code-autocomplete](https://github.com/shibing624/code-autocomplete)，support GPT2 model, usage：
+
+```python
+from autocomplete.gpt2_coder import GPT2Coder
+
+m = GPT2Coder("shibing624/code-autocomplete-distilgpt2-python")
+print(m.generate('import torch.nn as')[0])
+```
+
+Also, use huggingface/transformers：
+
+*Please use 'GPT2' related functions to load this model!*
+
+```python
+import os
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+tokenizer = GPT2Tokenizer.from_pretrained("shibing624/code-autocomplete-distilgpt2-python")
+model = GPT2LMHeadModel.from_pretrained("shibing624/code-autocomplete-distilgpt2-python")
+
+prompts = [
+    """from torch import nn
+    class LSTM(Module):
+        def __init__(self, *,
+                     n_tokens: int,
+                     embedding_size: int,
+                     hidden_size: int,
+                     n_layers: int):""",
+    """import numpy as np
+    import torch
+    import torch.nn as""",
+    "import java.util.ArrayList",
+    "def factorial(n):",
+]
+for prompt in prompts:
+    input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors='pt')
+    outputs = model.generate(input_ids=input_ids,
+                             max_length=64 + len(prompt),
+                             temperature=1.0,
+                             top_k=50,
+                             top_p=0.95,
+                             repetition_penalty=1.0,
+                             do_sample=True,
+                             num_return_sequences=1,
+                             length_penalty=2.0,
+                             early_stopping=True)
+    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    print(decoded)
+    print("=" * 20)
+```
+
+output:
+```shell
+from torch import nn
+    class LSTM(Module):
+        def __init__(self, *,
+                     n_tokens: int,
+                     embedding_size: int,
+                     hidden_size: int,
+                     n_layers: int):
+            self.embedding_size = embedding_size
+====================
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+```
+
+Model files：
+```
+code-autocomplete-distilgpt2-python
+├── config.json
+├── merges.txt
+├── pytorch_model.bin
+├── special_tokens_map.json
+├── tokenizer_config.json
+└── vocab.json
+```
+
+### Train data
+#### pytorch_awesome projects source code
+
+download [code-autocomplete](https://github.com/shibing624/code-autocomplete),
+```shell
+cd autocomplete
+python create_dataset.py
+```
+
+If you want train code-autocomplete GPT2 model，refer [https://github.com/shibing624/code-autocomplete/blob/main/autocomplete/gpt2_coder.py](https://github.com/shibing624/code-autocomplete/blob/main/autocomplete/gpt2_coder.py)
+
+
+### About GPT2
+
+Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
+
+Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
+[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
+and first released at [this page](https://openai.com/blog/better-language-models/).
+
+Disclaimer: The team releasing GPT-2 also wrote a
+[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
+has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
+
+
+## Citation
+
+```latex
+@misc{code-autocomplete,
+  author = {Xu Ming},
+  title = {code-autocomplete: Code AutoComplete with GPT model},
+  year = {2022},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  url = {https://github.com/shibing624/code-autocomplete},
+}
+```