metadata
license: apache-2.0
widget:
- text: |-
<|endoftext|>
def load_excel(path):
return pd.read_excel(path)
# docstring
"""
Basic info
model based Salesforce/codegen-350M-mono
fine-tuned with data codeparrot/github-code-clean
data filter by python
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
model_type = 'kdf/python-docstring-generation'
tokenizer = AutoTokenizer.from_pretrained(model_type)
model = AutoModelForCausalLM.from_pretrained(model_type)
inputs = tokenizer('''<|endoftext|>
def load_excel(path):
return pd.read_excel(path)
# docstring
"""''', return_tensors='pt')
doc_max_length = 128
generated_ids = model.generate(
**inputs,
max_length=inputs.input_ids.shape[1] + doc_max_length,
do_sample=False,
return_dict_in_generate=True,
num_return_sequences=1,
output_scores=True,
pad_token_id=50256,
eos_token_id=50256 # <|endoftext|>
)
ret = tokenizer.decode(generated_ids.sequences[0], skip_special_tokens=False)
print(ret)